{"id":438,"date":"2026-09-26T00:16:35","date_gmt":"2026-09-25T15:16:35","guid":{"rendered":"https:\/\/www.comm.tcu.ac.jp\/mikawalab\/?page_id=438"},"modified":"2026-09-26T11:20:50","modified_gmt":"2026-09-26T02:20:50","slug":"publications","status":"publish","type":"page","link":"https:\/\/www.comm.tcu.ac.jp\/mikawalab\/publications","title":{"rendered":"\u7814\u7a76\u696d\u7e3e"},"content":{"rendered":"\r\n<style>\r\n  body:has(.ml-site) #site-header,\r\n  body:has(.ml-site) .page-header,\r\n  body:has(.ml-site) #breadcrumb,\r\n  body:has(.ml-site) .site-footer,\r\n  body:has(.ml-site) .vk-mobile-nav-menu-btn {\r\n    display: none;\r\n  }\r\n\r\n  body:has(.ml-site) .site-body {\r\n    padding: 0;\r\n  }\r\n\r\n  body:has(.ml-site) .site-body-container {\r\n    max-width: none;\r\n    width: 100%;\r\n    padding: 0;\r\n  }\r\n\r\n  body:has(.ml-site) .main-section {\r\n    width: 100%;\r\n    margin: 0;\r\n  }\r\n\r\n  body:has(.ml-site) .entry-body {\r\n    margin: 0;\r\n  }\r\n\r\n  .ml-site {\r\n    color: #08264e;\r\n    font-family: Arial, \"Noto Sans JP\", sans-serif;\r\n    font-size: 15px;\r\n    line-height: 1.9;\r\n    overflow: hidden;\r\n  }\r\n\r\n  .ml-site * {\r\n    box-sizing: border-box;\r\n  }\r\n\r\n  .ml-site a {\r\n    color: #0061c9;\r\n    text-decoration: none;\r\n  }\r\n\r\n  .ml-site a:hover {\r\n    text-decoration: underline;\r\n  }\r\n\r\n  .ml-site a:focus-visible,\r\n  .ml-site summary:focus-visible {\r\n    outline: 3px solid #1687dd;\r\n    outline-offset: 4px;\r\n  }\r\n\r\n  .ml-wrap {\r\n    max-width: 1140px;\r\n    padding: 0 24px;\r\n    margin: auto;\r\n  }\r\n\r\n  .ml-site h1,\r\n  .ml-site h2,\r\n  .ml-site h3 {\r\n    background: none;\r\n    border: 0;\r\n    box-shadow: none;\r\n    padding: 0;\r\n    font-weight: 700;\r\n    color: #08264e;\r\n    line-height: 1.5;\r\n  }\r\n\r\n  .ml-site h1:after,\r\n  .ml-site h2:after,\r\n  .ml-site h3:after {\r\n    display: none;\r\n  }\r\n\r\n  .ml-site h2 {\r\n    font-size: 25px;\r\n    margin: 0 0 20px;\r\n  }\r\n\r\n  .ml-site h3 {\r\n    font-size: 19px;\r\n    margin: 0 0 12px;\r\n  }\r\n\r\n  .ml-site p {\r\n    margin: 0 0 18px;\r\n  }\r\n\r\n  .ml-site ul {\r\n    padding-left: 1.4em;\r\n  }\r\n\r\n  .ml-site li {\r\n    margin-bottom: 7px;\r\n  }\r\n\r\n  .ml-site img {\r\n    max-width: 100%;\r\n    height: auto;\r\n  }\r\n\r\n  .ml-top .ml-wrap,\r\n  .ml-bottom-row {\r\n    display: flex;\r\n    justify-content: space-between;\r\n    align-items: center;\r\n    gap: 20px;\r\n  }\r\n\r\n  .ml-top .ml-wrap {\r\n    min-height: 74px;\r\n  }\r\n\r\n  .ml-brand {\r\n    display: flex;\r\n    align-items: center;\r\n    gap: 12px;\r\n    color: #062149 !important;\r\n    font-size: 26px;\r\n    font-weight: 700;\r\n    letter-spacing: .02em;\r\n    white-space: nowrap;\r\n  }\r\n\r\n  .ml-mark {\r\n    font-size: 46px;\r\n    font-weight: 900;\r\n    font-style: italic;\r\n    line-height: 1;\r\n    background: linear-gradient(125deg, #072b79 25%, #007adc 50%, #73c3f5 70%);\r\n    background-clip: text;\r\n    -webkit-background-clip: text;\r\n    -webkit-text-fill-color: transparent;\r\n    letter-spacing: -.15em;\r\n    padding-right: 8px;\r\n  }\r\n\r\n  .ml-menu {\r\n    display: flex;\r\n    gap: 18px;\r\n    align-items: center;\r\n    font-size: 12px;\r\n    font-weight: 700;\r\n    white-space: nowrap;\r\n  }\r\n\r\n  .ml-menu a {\r\n    color: #08264e;\r\n  }\r\n\r\n  .ml-menu a[aria-current] {\r\n    color: #0069dc;\r\n    border-bottom: 2px solid #0069dc;\r\n    padding: 8px 0;\r\n  }\r\n\r\n  .ml-mobile {\r\n    display: none;\r\n  }\r\n\r\n  .ml-banner {\r\n    background: linear-gradient(90deg, rgba(4, 31, 59, .92), rgba(4, 43, 75, .65) 50%, rgba(4, 43, 75, .12)), url('https:\/\/www.comm.tcu.ac.jp\/mikawalab\/wordpress\/wp-content\/uploads\/2026\/09\/mikawa-campus-hero-v2.png') center 12%\/cover;\r\n    color: white;\r\n    padding: 36px 0;\r\n    min-height: 178px;\r\n  }\r\n\r\n  .ml-banner h1 {\r\n    font-size: 34px;\r\n    color: white;\r\n    margin: 0 0 4px;\r\n  }\r\n\r\n  .ml-banner p {\r\n    font-size: 13px;\r\n    letter-spacing: .12em;\r\n    margin: 0;\r\n    color: #e2f1ff;\r\n  }\r\n\r\n  .ml-crumb {\r\n    font-size: 11px;\r\n    color: #607995;\r\n    padding-top: 15px;\r\n  }\r\n\r\n  .ml-content {\r\n    padding-top: 38px;\r\n    padding-bottom: 55px;\r\n    min-height: 420px;\r\n  }\r\n\r\n  .ml-intro {\r\n    font-size: 17px;\r\n    line-height: 2;\r\n    max-width: 800px;\r\n    margin-bottom: 28px !important;\r\n  }\r\n\r\n  .ml-panel {\r\n    background: #f1f7fc;\r\n    padding: 28px;\r\n    border-radius: 3px;\r\n    margin: 28px 0;\r\n  }\r\n\r\n  .ml-split {\r\n    display: grid;\r\n    grid-template-columns: 1.35fr 1fr;\r\n    gap: 38px;\r\n    align-items: center;\r\n  }\r\n\r\n  .ml-photo {\r\n    margin: 0;\r\n  }\r\n\r\n  .ml-site .ml-photo img {\r\n    display: block;\r\n    width: 100%;\r\n    height: 270px;\r\n    object-fit: cover;\r\n    object-position: 50% 24%;\r\n    border-radius: 3px;\r\n  }\r\n\r\n  .ml-section {\r\n    margin-top: 38px;\r\n  }\r\n\r\n  .ml-site .wp-block-button__link {\r\n    background: white;\r\n    color: #0061c9;\r\n    border: 1px solid #0876db;\r\n    border-radius: 28px;\r\n    padding: 8px 24px;\r\n    font-size: 13px;\r\n    font-weight: 700;\r\n    line-height: 1.7;\r\n  }\r\n\r\n  .ml-site .wp-block-buttons {\r\n    gap: 12px;\r\n    margin: 18px 0 0;\r\n  }\r\n\r\n  .ml-site .wp-block-button__link span {\r\n    margin-left: 15px;\r\n  }\r\n\r\n  .ml-theme {\r\n    border-top: 1px solid #d9e6f1;\r\n    padding: 30px 0;\r\n    margin: 0;\r\n    display: grid;\r\n    grid-template-columns: 90px 1fr;\r\n    gap: 24px;\r\n  }\r\n\r\n  .ml-theme-number {\r\n    font-size: 38px;\r\n    font-weight: 700;\r\n    line-height: 1.3;\r\n    color: #1474c4;\r\n  }\r\n\r\n  .ml-theme-number small {\r\n    display: block;\r\n    font-size: 10px;\r\n    letter-spacing: .1em;\r\n    color: #6988a8;\r\n  }\r\n\r\n  .ml-theme p {\r\n    max-width: 820px;\r\n  }\r\n\r\n  .ml-theme ul {\r\n    margin-bottom: 0;\r\n  }\r\n\r\n  .ml-profile {\r\n    display: grid;\r\n    grid-template-columns: 220px 1fr;\r\n    gap: 40px;\r\n    align-items: center;\r\n    margin-bottom: 36px;\r\n  }\r\n\r\n  .ml-profile figure {\r\n    margin: 0;\r\n  }\r\n\r\n  .ml-site .ml-profile img {\r\n    display: block;\r\n    width: 220px;\r\n    height: 250px;\r\n    object-fit: cover;\r\n    object-position: center top;\r\n  }\r\n\r\n  .ml-profile h2 {\r\n    font-size: 32px;\r\n    margin-bottom: 2px;\r\n  }\r\n\r\n  .ml-en {\r\n    font-size: 12px;\r\n    letter-spacing: .12em;\r\n    color: #6885a3;\r\n  }\r\n\r\n  .ml-tags {\r\n    display: flex;\r\n    flex-wrap: wrap;\r\n    gap: 10px;\r\n    list-style: none;\r\n    padding: 0 !important;\r\n    margin: 16px 0 0;\r\n  }\r\n\r\n  .ml-tags li {\r\n    font-size: 12px;\r\n    background: #eaf3fb;\r\n    padding: 4px 13px;\r\n    border-radius: 2px;\r\n  }\r\n\r\n  .ml-site .wp-block-table {\r\n    margin: 0 0 25px;\r\n    overflow-x: auto;\r\n  }\r\n\r\n  .ml-site table {\r\n    width: 100%;\r\n    border-collapse: collapse;\r\n    font-size: 14px;\r\n  }\r\n\r\n  .ml-site table th,\r\n  .ml-site table td {\r\n    padding: 14px 16px;\r\n    border: 0;\r\n    border-bottom: 1px solid #dde7f0;\r\n    vertical-align: top;\r\n    text-align: left;\r\n  }\r\n\r\n  .ml-site table th {\r\n    width: 145px;\r\n    font-weight: 600;\r\n    background: #f3f7fb;\r\n    white-space: nowrap;\r\n  }\r\n\r\n  .ml-pubdetails {\r\n    border: 1px solid #dce7f2;\r\n    border-radius: 3px;\r\n    margin: 0 0 14px;\r\n    background: white;\r\n  }\r\n\r\n  .ml-pubdetails summary {\r\n    cursor: pointer;\r\n    padding: 20px 24px;\r\n    font-size: 20px;\r\n    font-weight: 700;\r\n    color: #0c3762;\r\n  }\r\n\r\n  .ml-pubdetails summary span {\r\n    float: right;\r\n    font-size: 14px;\r\n    font-weight: 400;\r\n    color: #66829f;\r\n  }\r\n\r\n  .ml-pubdetails[open] summary {\r\n    border-bottom: 1px solid #e2ebf4;\r\n    background: #f2f8fd;\r\n  }\r\n\r\n  .ml-pubdetails ol {\r\n    margin: 8px 28px 12px 58px;\r\n    padding: 0;\r\n    overflow-wrap: anywhere;\r\n  }\r\n\r\n  .ml-pubdetails li {\r\n    padding: 16px 0 16px 8px;\r\n    margin: 0;\r\n    border-bottom: 1px solid #edf2f7;\r\n    line-height: 1.7;\r\n  }\r\n\r\n  .ml-pubdetails li:last-child {\r\n    border-bottom: 0;\r\n  }\r\n\r\n  .ml-pubdetails li::marker {\r\n    color: #8aa3bb;\r\n    font-size: 13px;\r\n  }\r\n\r\n  .ml-pub-au,\r\n  .ml-pub-ti,\r\n  .ml-pub-src {\r\n    display: block;\r\n    font-size: 13pt;\r\n    font-weight: 400;\r\n    color: #56708b;\r\n  }\r\n\r\n  .ml-note {\r\n    font-size: 12px;\r\n    color: #647d96;\r\n  }\r\n\r\n  .ml-jumps {\r\n    display: flex;\r\n    gap: 12px;\r\n    flex-wrap: wrap;\r\n    margin: 0 0 24px;\r\n  }\r\n\r\n  .ml-jumps a {\r\n    padding: 5px 14px;\r\n    border: 1px solid #dce7f2;\r\n    border-radius: 3px;\r\n    font-size: 12px;\r\n  }\r\n\r\n  .ml-news-list .wp-block-post-template {\r\n    margin: 0;\r\n    padding: 0;\r\n    list-style: none;\r\n  }\r\n\r\n  .ml-news-row {\r\n    display: grid;\r\n    grid-template-columns: 160px 1fr;\r\n    gap: 24px;\r\n    align-items: center;\r\n    padding: 23px 0;\r\n    border-bottom: 1px solid #dce7f2;\r\n  }\r\n\r\n  .ml-news-row figure {\r\n    margin: 0;\r\n    min-height: 100px;\r\n    background: #eef5fc;\r\n  }\r\n\r\n  .ml-news-row figure:empty:before {\r\n    content: \"MIKAWA LAB. \/ NEWS\";\r\n    display: flex;\r\n    align-items: center;\r\n    justify-content: center;\r\n    height: 100px;\r\n    color: #527fa7;\r\n    font-size: 10px;\r\n    letter-spacing: .07em;\r\n  }\r\n\r\n  .ml-news-row figure img {\r\n    width: 160px;\r\n    height: 100px;\r\n    object-fit: cover;\r\n  }\r\n\r\n  .ml-news-row h2 {\r\n    font-size: 17px;\r\n    margin: 5px 0;\r\n  }\r\n\r\n  .ml-news-row .wp-block-post-date {\r\n    font-size: 12px;\r\n    color: #6583a0;\r\n  }\r\n\r\n  .ml-news-row .wp-block-post-terms {\r\n    font-size: 11px;\r\n    display: inline-block;\r\n    color: #1474c4;\r\n  }\r\n\r\n  .ml-news-list .wp-block-query-pagination {\r\n    display: flex;\r\n    justify-content: center;\r\n    gap: 20px;\r\n    margin-top: 30px;\r\n    font-size: 13px;\r\n  }\r\n\r\n  .ml-news-list .page-numbers {\r\n    padding: 5px 10px;\r\n  }\r\n\r\n  .ml-news-list .current {\r\n    background: #075bb4;\r\n    color: white;\r\n  }\r\n\r\n  .ml-map {\r\n    width: 100%;\r\n    height: 320px;\r\n    border: 0;\r\n    border-radius: 3px;\r\n  }\r\n\r\n  .ml-contact-box {\r\n    border: 1px solid #dce7f2;\r\n    background: #f3f8fd;\r\n    padding: 30px;\r\n    margin: 24px 0;\r\n  }\r\n\r\n  .ml-mail {\r\n    font-size: 27px;\r\n    font-weight: 700;\r\n    overflow-wrap: anywhere;\r\n  }\r\n\r\n  .ml-bottom {\r\n    background: #f5f9fd;\r\n    padding: 22px 0;\r\n  }\r\n\r\n  .ml-bottom .ml-brand {\r\n    font-size: 20px;\r\n  }\r\n\r\n  .ml-bottom .ml-mark {\r\n    font-size: 36px;\r\n  }\r\n\r\n  .ml-bottom p {\r\n    font-size: 10px;\r\n    color: #718db0;\r\n    margin: 4px 0 0;\r\n  }\r\n\r\n  .ml-bottom-logo {\r\n    width: 190px;\r\n    display: block;\r\n    flex: none;\r\n  }\r\n\r\n  .ml-bottom-nav {\r\n    display: flex;\r\n    gap: 20px;\r\n    flex-wrap: wrap;\r\n    font-size: 11px;\r\n    margin-bottom: 22px;\r\n  }\r\n\r\n  .ml-bottom-nav a {\r\n    color: #31587f;\r\n  }\r\n\r\n  @media (max-width: 1050px) {\r\n    .ml-menu {\r\n      gap: 10px;\r\n      font-size: 11px;\r\n    }\r\n\r\n    .ml-brand {\r\n      font-size: 22px;\r\n    }\r\n  }\r\n\r\n  @media (max-width: 767px) {\r\n    .ml-wrap {\r\n      padding-left: 20px;\r\n      padding-right: 20px;\r\n    }\r\n\r\n    .ml-top .ml-wrap {\r\n      min-height: 66px;\r\n    }\r\n\r\n    .ml-menu {\r\n      display: none;\r\n    }\r\n\r\n    .ml-mobile {\r\n      display: block;\r\n    }\r\n\r\n    .ml-mobile summary {\r\n      font-size: 13px;\r\n      color: #0756a3;\r\n      cursor: pointer;\r\n    }\r\n\r\n    .ml-mobile[open] nav {\r\n      display: grid;\r\n      gap: 12px;\r\n      position: absolute;\r\n      left: 0;\r\n      right: 0;\r\n      z-index: 15;\r\n      background: #fff;\r\n      padding: 24px;\r\n      box-shadow: 0 10px 20px #08264e14;\r\n    }\r\n\r\n    .ml-banner {\r\n      min-height: 150px;\r\n      padding: 28px 0;\r\n      background-position: 70% 25%;\r\n    }\r\n\r\n    .ml-banner h1 {\r\n      font-size: 28px;\r\n    }\r\n\r\n    .ml-content {\r\n      padding-top: 28px;\r\n      padding-bottom: 38px;\r\n    }\r\n\r\n    .ml-intro {\r\n      font-size: 15px;\r\n    }\r\n\r\n    .ml-site h2 {\r\n      font-size: 23px;\r\n    }\r\n\r\n    .ml-split,\r\n    .ml-profile {\r\n      grid-template-columns: 1fr;\r\n      gap: 24px;\r\n    }\r\n\r\n    .ml-site .ml-photo img {\r\n      height: 210px;\r\n    }\r\n\r\n    .ml-panel {\r\n      padding: 22px;\r\n    }\r\n\r\n    .ml-theme {\r\n      grid-template-columns: 44px 1fr;\r\n      gap: 16px;\r\n      padding: 24px 0;\r\n    }\r\n\r\n    .ml-theme-number {\r\n      font-size: 28px;\r\n    }\r\n\r\n    .ml-theme-number small {\r\n      font-size: 8px;\r\n    }\r\n\r\n    .ml-site .ml-profile img {\r\n      width: 155px;\r\n      height: 175px;\r\n    }\r\n\r\n    .ml-profile h2 {\r\n      font-size: 28px;\r\n    }\r\n\r\n    .ml-site table th {\r\n      width: 92px;\r\n      white-space: normal;\r\n    }\r\n\r\n    .ml-site table th,\r\n    .ml-site table td {\r\n      padding: 11px 9px;\r\n      font-size: 12px;\r\n    }\r\n\r\n    .ml-pubdetails summary {\r\n      font-size: 18px;\r\n      padding: 16px;\r\n    }\r\n\r\n    .ml-pubdetails ol {\r\n      margin: 4px 16px 8px 40px;\r\n    }\r\n\r\n    .ml-pubdetails li {\r\n      padding: 13px 0 13px 4px;\r\n    }\r\n\r\n    .ml-pub-au,\r\n    .ml-pub-ti,\r\n    .ml-pub-src {\r\n      font-size: 12px;\r\n    }\r\n\r\n    .ml-news-row {\r\n      grid-template-columns: 90px 1fr;\r\n      gap: 14px;\r\n    }\r\n\r\n    .ml-news-row figure img {\r\n      width: 90px;\r\n      height: 82px;\r\n    }\r\n\r\n    .ml-news-row figure {\r\n      min-height: 82px;\r\n    }\r\n\r\n    .ml-news-row figure:empty:before {\r\n      height: 82px;\r\n      font-size: 8px;\r\n      text-align: center;\r\n    }\r\n\r\n    .ml-news-row h2 {\r\n      font-size: 14px;\r\n    }\r\n\r\n    .ml-map {\r\n      height: 270px;\r\n    }\r\n\r\n    .ml-mail {\r\n      font-size: 23px;\r\n    }\r\n\r\n    .ml-contact-box {\r\n      padding: 22px;\r\n    }\r\n\r\n    .ml-bottom-row {\r\n      align-items: flex-start;\r\n      gap: 14px;\r\n    }\r\n\r\n    .ml-bottom .ml-brand {\r\n      font-size: 16px;\r\n      gap: 8px;\r\n    }\r\n\r\n    .ml-bottom .ml-mark {\r\n      font-size: 30px;\r\n    }\r\n\r\n    .ml-bottom-logo {\r\n      width: 130px;\r\n      max-width: 38%;\r\n    }\r\n\r\n    .ml-bottom-nav {\r\n      gap: 10px 18px;\r\n    }\r\n  }\r\n\r\n  .ml-news-row:not(:has(figure)):before {\r\n    content: \"MIKAWA LAB. \/ NEWS\";\r\n    display: flex;\r\n    align-items: center;\r\n    justify-content: center;\r\n    height: 100px;\r\n    background: linear-gradient(120deg, #e2f0fc, #cce5f4);\r\n    font-size: 10px;\r\n    color: #527fa7;\r\n    letter-spacing: .05em;\r\n  }\r\n\r\n  .ml-news-row>div {\r\n    min-width: 0;\r\n  }\r\n\r\n  @media (max-width: 767px) {\r\n    .ml-news-row: not(:has(figure)):before {\r\n      height: 82px;\r\n      font-size: 8px;\r\n      text-align: center;\r\n    }\r\n  }\r\n<\/style>\r\n<div class=\"ml-site\" id=\"ml-top\">\r\n  <header class=\"ml-top\">\r\n    <div class=\"ml-wrap\">\r\n      <a class=\"ml-brand\" href=\"https:\/\/www.comm.tcu.ac.jp\/mikawalab\/\"><span class=\"ml-mark\" aria-hidden=\"true\">M<\/span> MIKAWA LAB.<\/a>\r\n      <nav class=\"ml-menu\" aria-label=\"\u30e1\u30a4\u30f3\u30ca\u30d3\u30b2\u30fc\u30b7\u30e7\u30f3\">\r\n        <a 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href=\"#journals\">\u5b66\u8853\u8ad6\u6587 38\u4ef6<\/a>\r\n        <a href=\"#international\">\u56fd\u969b\u4f1a\u8b70 78\u4ef6<\/a>\r\n        <a href=\"#presentations\">\u56fd\u5185\u5b66\u4f1a\u30fb\u7814\u7a76\u4f1a 156\u4ef6<\/a>\r\n        <a href=\"#other\">\u89e3\u8aac\u30fb\u7d00\u8981\u7b49 3\u4ef6<\/a>\r\n        <a href=\"#books\">\u66f8\u7c4d 1\u4ef6<\/a>\r\n      <\/nav>\r\n\r\n      <details class=\"ml-pubdetails\" id=\"journals\">\r\n        <summary>\u5b66\u8853\u8ad6\u6587<span>38\u4ef6<\/span><\/summary>\r\n        <ol>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLatent structure analysis for relational data with attribute information to multiple domains,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Nonlinear Theory and Its Applications, IEICE, Vol. 17, No. 3, pp. 854\u2013874, Jul. 2026.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cHigh-dimensional Labeled Data Embedding and Visualization Method Using von Mises Distribution,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Total Quality Science, Vol. 11, No. 1, pp. 22\u201329, Nov. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA study on latent structural models for binary relational data with attribute information,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Nonlinear Theory and Its Applications, IEICE, Vol. 15, No. 2, pp. 335\u2013353, Apr. 2024.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9752\u6728\u7ae0\u609f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cWeb\u95b2\u89a7\u5c65\u6b74\u306b\u57fa\u3065\u304f\u30bf\u30fc\u30b2\u30c6\u30a3\u30f3\u30b0\u306e\u305f\u3081\u306e\u5c5e\u6027\u30e9\u30d9\u30eb\u5b66\u7fd2\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 73\uff0cNo. 1\uff0cpp. 1\u201314\uff0c2022\u5e744\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">R. Uehara, T. Matsumoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Prediction Model of Runs Allowed Based on Latent-Class Markov Chain for Starters of Professional Baseball Pitchers,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Total Quality Science, Vol. 7, No. 2, pp. 69\u201381, Feb. 2022.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u4f50\u3005\u6728\u5317\u90fd\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u4fa1\u683c\u4fdd\u6301\u671f\u9593\u9069\u6b63\u5316\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u3068\u5b9f\u8a3c\u7684\u52b9\u679c\u691c\u8a3c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 63\uff0cNo. 1\uff0cpp. 218\u2013230\uff0c2022\u5e741\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Sugisaki, Y. Nishio, K. Mikawa, M. Goto, and T. Sakurai,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAnalysis of Entry Behavior of Students on Job Boards in Japan based on Factorization Machine Considering the Interaction among Features,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Cogent Engineering, Vol. 8, No. 1, 1988381, Oct. 2021.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Yasui, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Extension of Semi-supervised Boosting to Multi-valued Classification Problems,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Total Quality Science, Vol. 6, No. 2, pp. 60\u201369, Feb. 2021.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u6749\u5d0e\u667a\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8ca9\u58f2\u5c65\u6b74\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u51fa\u54c1\u4fa1\u683c\u63a8\u5b9a\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 62\uff0cNo. 2\uff0cpp. 796\u2013808\uff0c2021\u5e741\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Sugisaki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cFactorization Machines Considering the Latent Characteristics behind Target Data,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Journal of Management Science and Applications, Vol. 5, No. 2, pp. 111\u2013128, Jan. 2021.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Q. Zhang, H. Yamashita, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAnalysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 19, No. 2, pp. 476\u2013483, Jun. 2020.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Okayama, H. Yamashita, K. Mikawa, M. Goto, and T. Yoshikai,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cRelational analysis model of weather conditions and sales patterns based on nonnegative tensor factorization,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">International Journal of Production Research, Vol. 58, No. 8, pp. 2477\u20132489, Dec. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u4e95\u4e00\u8cb4\uff0c\u4e2d\u91ce\u4fee\u5e73\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5468\u671f\u6027\u3068\u30a4\u30d9\u30f3\u30c8\u52b9\u679c\u306b\u7740\u76ee\u3057\u305f\u6d88\u8cbb\u8005\u306e\u8cfc\u8cb7\u884c\u52d5\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c\uff0cVol. 28\uff0cNo. 2\uff0cpp. 69\u201387\uff0c2019\u5e749\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Nagamori, K. Mikawa, M. Goto, and T. Ogihara,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Analytic Model to Represent Relation between Finish Date of Job-Hunting and Time-Series Variation of Entry Tendencies,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 18, No. 3, pp. 292\u2013304, Sep. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4ec1\u30ce\u5e73\u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8ca9\u58f2\u5c65\u6b74\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u8ca9\u58f2\u4fa1\u683c\u4e88\u6e2c\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 60\uff0cNo. 4\uff0cpp. 1151\u20131161\uff0c2019\u5e744\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u99ac\u8cc0\u5d69\u58eb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6c17\u8c61\u60c5\u5831\u3068Tweet\u30c7\u30fc\u30bf\u306e\u7d71\u5408\u7684\u5206\u6790\u306b\u3088\u308b\u4f53\u611f\u6c17\u6e29\u306e\u5b9a\u91cf\u5316\u3068\u305d\u306e\u9700\u8981\u4e88\u6e2c\u3078\u306e\u5fdc\u7528\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8cD\uff0cVol. J101-D\uff0cNo. 7\uff0cpp. 1037\u20131051\uff0c2018\u5e747\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u95a2\u53e3\u3042\u3086\u307f\uff0c\u4ec1\u30ce\u5e73\u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u63a8\u5b9a\u8cfc\u8cb7\u78ba\u7387\u3068\u4e88\u6e2c\u8a55\u4fa1\u5024\u3092\u30d0\u30e9\u30f3\u30b9\u3059\u308b\u610f\u5916\u6027\u6307\u6a19\u306b\u57fa\u3065\u304f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c\uff0cVol. 27\uff0cNo. 1\uff0cpp. 67\u201378\uff0c2018\u5e746\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u677e\u5d5c\u7950\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30de\u30eb\u30b3\u30d5\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304fEC\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u65bd\u7b56\u5b9f\u65bd\u52b9\u679c\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 58\uff0cNo. 12\uff0cpp. 2034\u20132045\uff0c2017\u5e7412\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Misawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAdaptive Prediction Method Based on Alternating Decision Forests with Considerations for Generalization Ability,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 16, No. 3, pp. 384\u2013391, Oct. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">L. Suzuki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMulti-Valued Classification of Text Data Based on an ECOC Approach Using a Ternary Orthogonal Table,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 16, No. 2, pp. 155\u2013164, Jul. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Yamamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 16, No. 2, pp. 165\u2013174, Jul. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e6f\u5ddd\u8f1d\u4e00\u6717\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c7\u30fc\u30bf\u306e\u8ee2\u9001\u5236\u5fa1\u306b\u57fa\u3065\u3044\u305f\u5206\u6563\u578bSVM\u306e\u52b9\u7387\u7684\u306a\u5b66\u7fd2\u624b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 68\uff0cNo. 2\uff0cpp. 86\u201398\uff0c2017\u5e747\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u85e4\u539f\u76f4\u5e83\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u95b2\u89a7\u53ca\u3073\u8cfc\u8cb7\u884c\u52d5\u3092\u540c\u6642\u306b\u8868\u73fe\u3059\u308b\u30a2\u30b9\u30da\u30af\u30c8\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8cfc\u8cb7\u4e88\u6e2c\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c\uff0cVol. 26\uff0cNo. 1\uff0cpp. 1\u201316\uff0c2017\u5e746\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u65e9\u5ddd\u771f\u592e\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u837b\u539f\u5927\u9678\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c64\u5225\u6728\u3068\u6df7\u5408\u30ef\u30a4\u30d6\u30eb\u5206\u5e03\u306b\u57fa\u3065\u304f\u5c31\u8077\u6d3b\u52d5\u7d42\u4e86\u6642\u671f\u306e\u5206\u6790\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 58\uff0cNo. 5\uff0cpp. 1189\u20131206\uff0c2017\u5e745\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Maga, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cData pair selection for accurate classification based on information-theoretic metric learning,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Journal of Management Science and Applications, Vol. 3, No. 1, pp. 61\u201374, Apr. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30ab\u30c6\u30b4\u30ea\u6bce\u306b\u7570\u306a\u308b\u8a08\u91cf\u884c\u5217\u3092\u7528\u3044\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 66\uff0cNo. 4\uff0cpp. 335\u2013347\uff0c2016\u5e741\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Goto, K. Mikawa, S. Hirasawa, M. Kobayashi, T. Suko, and S. Horii,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA New Latent Class Model for Analysis of Purchasing and Browsing Histories on EC Sites,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 14, No. 4, pp. 335\u2013346, Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306b\u57fa\u3065\u304f\u21131\u6b63\u5247\u5316\u3092\u7528\u3044\u305f\u8a08\u91cf\u884c\u5217\u306e\u5b66\u7fd2\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 66\uff0cNo. 3\uff0cpp. 230\u2013239\uff0c2015\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cRegularized Distance Metric Learning for the Document Classification and its Application,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 66, No. 2E, pp. 190\u2013203, Jul. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u4e95\u8cb4\u88d5\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8a55\u4fa1\u3068\u8cfc\u8cb7\u306e\u4e21\u5c65\u6b74\u30c7\u30fc\u30bf\u306e\u5b66\u7fd2\u306b\u3088\u308b\u78ba\u7387\u7684\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u63a8\u5b9a\u7cbe\u5ea6\u5411\u4e0a\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 65\uff0cNo. 4\uff0cpp. 286\u2013293\uff0c2015\u5e741\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e0b\u6751\u826f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5927\u898f\u6a21\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u306e\u5206\u985e\u4f53\u7cfb\u5316\u306e\u305f\u3081\u306e\u6a5f\u68b0\u5b66\u7fd2\u306b\u57fa\u3065\u304f\u534a\u81ea\u52d5\u5206\u985e\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 65\uff0cNo. 2\uff0cpp. 51\u201360\uff0c2014\u5e747\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Suzuki, G. Kumoi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Design of Recommendation Based on Flexible Mixture Model Considering Purchasing Interest and Post-Purchase Satisfaction,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 64, No. 4E, pp. 570\u2013578, Jan. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e95\u6ca2\u7950\u4ecb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30d9\u30fc\u30b9\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308b\u78ba\u7387\u6f5c\u5728\u7a7a\u9593\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306e\u8a55\u4fa1\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 22\uff0cNo. 2\uff0cpp. 1\u201322\uff0c2013\u5e749\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Ogihara, K. Mikawa, G. Hosoya, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMulti-valued Document Classification based on coding theory,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">China-USA Business Review, Vol. 12, No. 9, pp. 911\u2013917, Sep. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u8352\u5ddd\u8cb4\u7d00\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u672a\u89b3\u6e2c\u30ab\u30c6\u30b4\u30ea\u3092\u542b\u3080\u6587\u66f8\u30c7\u30fc\u30bf\u306e\u81ea\u52d5\u5206\u985e\u624b\u6cd5\u306b\u95a2\u3059\u308b\u7814\u7a76\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8cD\uff0cVol. J96-D\uff0cNo. 8\uff0cpp. 1956\u20131959\uff0c2013\u5e748\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Industrial Engineering &amp; Management Systems, Vol. 11, No. 1, pp. 87\u201393, Jan. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5897\u4e95\u5fe0\u5e78\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u9867\u5ba2\u30ed\u30a4\u30e4\u30eb\u30c6\u30a3\u69cb\u9020\u56f3\u306b\u57fa\u3065\u304f\u91cd\u8981\u8981\u56e0\u306e\u5b9a\u91cf\u5316\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 59\uff0cNo. 5\uff0cpp. 365\u2013375\uff0c2008\u5e7412\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u9ad8\u6a4b\u52c9\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u9867\u5ba2\u30ed\u30a4\u30e4\u30eb\u30c6\u30a3\u306e\u69cb\u9020\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 58\uff0cNo. 3\uff0cpp. 182\u2013192\uff0c2007\u5e748\u6708\uff0e<\/span>\r\n          <\/li>\r\n        <\/ol>\r\n      <\/details>\r\n\r\n      <details class=\"ml-pubdetails\" id=\"international\">\r\n        <summary>\u56fd\u969b\u4f1a\u8b70<span>78\u4ef6<\/span><\/summary>\r\n        <ol>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Multi-Domain Relational Data Analysis Model with Varying Latent Dimensions,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">2026 2nd International Conference on Consumer Technology (ICCT-Pacific), pp. 172\u2013175, Mar. 2026.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Fukuda and K. Mikawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cExtending the Semi-supervised Signed Stochastic Block Model to Multiple Domains,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 8th Asian Conference of Management Science &amp; Applications (ACMSA 2025), Dec. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Attribute Similarity Interpretation Method Based on Customer Behavior Using the ECOC Approach,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 8th Asian Conference of Management Science &amp; Applications (ACMSA 2025), Shanghai, China, ID: 61, Dec. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cProposal of an Explainable Multiclass Classification Method Using Neural Networks with ECOC Approach,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 52nd International Conference on Computers and Industrial Engineering (CIE52), INSA Lyon, France, CIE2025-125, pp. 621\u2013627, Oct. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLatent Structure Analysis for Relational Data with Attribute Information to Multiple Domains,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2025 International Symposium on Nonlinear Theory and Its Applications, pp. 618\u2013621, Oct. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cIntegrating Attribute Information into Relational Data: A Latent Structure Approach,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">2025 1st International Conference on Consumer Technology (ICCT-Pacific), pp. 1\u20134, Mar. 2025.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cHigh-dimensional Supervised Embedding and Visualization Method using von Mises Distribution,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 22nd Asian Network for Quality Congress (ANQ 2024), Sep. 2024.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Goto, M. Kobayashi, T. Moriguchi, Y. Seki, H. Suzuki, T. Namatame, K. Nakata, A. Ishigaki, M. Ueda, K. Sato, K. Mikawa, H. Yamashita, T. Tabata, T. Yang, A. Yamagiwa, and Y. Tajiri,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cOnline Flipped Conference Based Data Science Education Program and Its Educational Effectiveness in Multi-University Collaboration,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 7th Asian Conference of Management Science &amp; Applications (ACMSA 2023), Dec. 2023.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, T. Sasaki, and A. Manada,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Latent Structural Models for Binary Relational Data with Attribute Information,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2023 International Symposium on Nonlinear Theory and Its Applications, Sep. 2023.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLearning and Estimation of Latent Structural Models Based on between-Data Metrics,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2022), Oct. 2022.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">R. Uehara, T. Matsumoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Prediction Model of Earned Runs Based on Latent Class Markov Chain for Starters of Professional Baseball Pitchers,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 18th Asian Network for Quality Congress (ANQ 2020), ANQ-138, Oct. 2020.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cFactorization Machine with Mixed Norm Regularization using ADMM,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 20th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2019), Dec. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Yasui, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Analytical Model of Consumers Purchasing Behavior Considering the Variety of Products,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2019), Oct. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Sakai, K. Yasui, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Extension of Semi-Supervised Boosting to Multiclass Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 17th Asian Network for Quality Congress (ANQ 2019), ID-180, Oct. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">I. Kuwata, T. Sugisaki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Estimation Model of Open Price for Second-hand Fashion Items Based on Sales History Data,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 17th Asian Network for Quality Congress (ANQ 2019), ID-179, Oct. 2019.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Nakano, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study of the Application of Canonical Correlation Forests to Text Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 19th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2018), Dec. 2018.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cProposal for an \u21131 regularized Factorization Machine,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 19th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2018), Dec. 2018.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Sugisaki, Y. Nishio, K. Mikawa, M. Goto, and T. Sakurai,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA New Entry Behavior Model of Student Users on Job Board for New Graduates Considering the Interaction between Features,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asian Network for Quality Congress (ANQ 2018), Sep. 2018.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Kobayashi, K. Mikawa, M. Goto, T. Matsushima, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCollaborative Filtering Based on the Latent Class Model for Attributes,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">16th IEEE International Conference on Machine Learning and Applications (ICMLA), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cDistance metric learning using each category centroid with nuclear norm regularization,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">2017 Symposium Series on Computational Intelligence (SSCI), 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Okayama, H. Yamashita, K. Mikawa, M. Goto, and T. Yoshikai,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cRelational Analysis Model of Weather Conditions and Sales Patterns Based on Nonnegative Matrix Factorization,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 18th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2017), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Arai, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cThe Bayesian Prediction Algorithm Using Logistic Regression,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 18th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2017), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Suzuki, N. Yamagishi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCharacteristics of a Word Segmentation Method Based on a State-transition Model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 18th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2017), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Ninohira, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Prediction Model of Selling Prices of Second-hand Fashion Items,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 18th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2017), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Nakano, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Analytical Method of Printer Data Log for Detecting Excellent Customers,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2017), Dec. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Kobayashi, K. Mikawa, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCollaborative Filtering Analysis of Consumption Behavior Based on the Latent Class Model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2017), Oct. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">D. Gyoten, M. Ninohira, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Ensemble Learning Focusing on Local Structure,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asian Network for Quality Congress (ANQ 2017), Sep. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Goto, K. Mikawa, M. Kobayashi, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Bayes prediction algorithm for model class composed of several subclasses,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2016 International Symposium on Information Theory and Its Applications (ISITA2016), pp. 285\u2013289, Oct. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cDistance metric learning based on different \u21131 regularized metric matrices in each category,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2016), Oct. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Matsuzaki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cModeling customer purchase behavior based on page transitions by latent class model for customer segmentation,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 17th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2016), Oct. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Yamamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA proposal of document recommendation based on topic model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 17th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2016), Oct. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Distance Metric Learning using Distance Structure among Category Centroids,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 17th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2016), Oct. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Nakano, S. Nagamori, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA study of Improving Classification Accuracy of k-nearest Neighbor Based on Local Metric Learning and Adaptive Weighted Ensemble,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads &amp; The 5th Institute of Industrial and Systems Engineering Asian Conference, Jul. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Arai, Y. Yamamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Approximate Bayesian Prediction Algorithm Based on Ensemble Learning,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 7th Forum for Council of Industrial Engineering and Logistics Management Department Heads &amp; The 5th Institute of Industrial and Systems Engineering Asian Conference, Jul. 2016.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Matsuzaki, K. Yamagami, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAnalysis of customer purchase behavior by using purchase history with discount coupon based on latent class model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">L. Suzuki, K. Yamagami, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMulti-valued Classification of Text Data based on ECOC Approach using Ternary Orthogonal Table,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Misawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAdaptive Prediction Method Based on Alternating Decision Forests considering Generalization Ability,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Yamagami, K. Mikawa, M. Goto, and T. Ogihara,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Statistical Prediction Model of Students&#x27; Success Date on Job Hunting Using Internet Portal Sites Data,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Nagamori, K. Yamagami, K. Mikawa, M. Goto, and T. Ogihara,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Finish Date Prediction of Job Hunting based on User Clustering Approach considering Time Series Variation of Entry Tendencies,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Yamamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal for the Classification Method of Documents Data with Unobserved Categories Considering Latent Topics,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 16th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2015), Dec. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study of Distance Metric Learning by Considering the Distances between Category Centroids,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2015 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2015), pp. 1645\u20131650, Oct. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Yukawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cThe Study of Distributed Support Vector Machine with Lower Time Computational Complexity,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2015), No. 47, Sep. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Maga, K. Yukawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cData pair selection for improving classification accuracy of Information-Theoretic Metric Learning,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2015), No. 22, Sep. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Q. Zhang, H. Yamashita, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAnalysis of Purchase History Data Based on a New Latent Class for RFM Analysis,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2015), No. 39, Sep. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">H. Auchi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Bayes Prediction Algorithm for the Model Class Conditioned by the Cumulative Number of Event Occurrences,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2015), Sep. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">H. Saito, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of Classification Method Based on Local Metric Matrices,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2015), Sep. 2015.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Goto, K. Mikawa, M. Kobayashi, S. Horii, T. Suko, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Analysis of Purchasing and Browsing Histories on an EC Site Based on a New Latent Class Model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 1st East Asia Workshop on Industrial Engineering, Nov. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Misawa, N. Fujiwara, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Prediction Method based on Weighted Ensemble of Decision Trees on Alternating Decision Forests,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">N. Fujiwara, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA New Estimation Method of Latent Class Model with High Accuracy by Using Both Browsing and Purchase Histories,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Yamagami, N. Fujiwara, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Statistical Model for Recommender System to Maximize Sales Amount Focusing on Characteristics of EC Site Data,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">H. Saito, F. Yamazaki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cDistance Metric Learning with Low Computational Complexity based on Ensemble of the Low-dimensional Matrixes,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Yukawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cStatistical Model Selection of Linear Regression for Privacy Preserving Data Mining,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 15th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2014), Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Goto, K. Minetoma, K. Mikawa, M. Kobayashi, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Modified Aspect Model for Simulation Analysis,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2014), pp. 1306\u20131311, Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, M. Kobayashi, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of \u21131 Regularized Distance Metric Learning for High Dimensional Sparse Vector Space,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2014), pp. 1985\u20131990, Oct. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Matsuzaki, S. Nagamori, K. Mikawa, M. Goto, and B. Bushell,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Comparative Survey on Sustainable Tourism Development Considering Regional Characteristics in Nepal,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The Conference of the 20th IICE, Jun. 2014.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cRegularized Distance Metric Learning and its Application to Knowledge Discovery,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Hayakawa, K. Mikawa, T. Ishida, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cStatistical Prediction Model of Students&#x27; Success on Job Hunting by Log Data Analysis,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Sakamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study on Recommender System Based on Latent Class Model for High Dimensional and Sparse Data,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">F. Yamazaki, S. Sakamoto, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cTraining Data Selection in Large Margin Nearest Neighbor Method for Classification Problems,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Ogihara, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMulti-valued Classification of Text Data based on ECOC Approach considering Parallel Processing,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Oi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study of Recommender Systems Based on the Latent Class Model Estimated by Combining Both Evaluation and Purchase Histories,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 14th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2013), Dec. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of Adaptive Metric Learning to Each Category Characteristics for Text Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">2013 RISP International Workshop on Nonlinear Circuits, Communications and Signal Processing, pp. 544\u2013547, Mar. 2013.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Optimal Weighting Method by Using the Category Information in Text Classification based on Metric Learning,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Izawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cThe Agent-Based Simulation Analysis of Collaborative Filtering Using Mixed Membership Stochastic Block Models,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Oi, T. Arakawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of Improved Na\u00efve Bayes Method for Collaborative Filtering by Introducing Clustering,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Ogihara, K. Mikawa, G. Hosoya, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMulti-valued Document Classification based on Generalized Bradley-Terry Classifiers Utilizing Accuracy Information,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">S. Sakamoto, Y. Izawa, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study for Recommender System based on Mixed and Constrained Latent Dirichlet Allocation,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Suzuki, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study of Recommender System to Improve Aggregate Diversity based on Latent Class Model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">G. Kumoi, K. Mikawa, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Study of Document Classification Based on Polya Mixture Distribution,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 13th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2012), No. 136-1, Dec. 2012.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, G. Kumoi, K. Suzuki, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of Extracting Unknown Information from Customer Review for SWOT Analysis,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Asian Conference of Management Science &amp; Applications (ACMSA 2011), ID-167, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">Y. Izawa, H. Sakaeda, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Recommender System Considering with Item Evaluation based on Mixed Membership Stochastic Block Models,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 12th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2011), ID-118, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">T. Suzuki, G. Kumoi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA study on the recommender system based on probabilistic latent model,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 12th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2011), ID-127, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAn Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 12th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2011), ID-141, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Hibi, G. Kumoi, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cAutomated source code plagiarism detection based on coding style model probability based on Relevance Vector Machine,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 12th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2011), ID-147, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">R. Odai, G. Kumoi, K. Mikawa, G. Hosoya, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMultivalued document classification by maximization of posterior probability based on Relevance Vector Machine,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">The 12th Asia Pacific Industrial Engineering and Management Systems Conference (APIEMS 2011), ID-205, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Mikawa, T. Ishida, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Proposal of Extended Cosine Measure for Distance Metric Learning in Text Classification,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Proc. 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC 2011), pp. 1741\u20131746, Oct. 2011.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">K. Suzuki, R. Horiuchi, Y. Kurishima, and K. Mikawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cA Participatory Process toward a Model for Environmental Sustainability,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">Salt of the Earth Conference, Oct. 2006.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">B. Bushell, Y. Kurishima, K. Mikawa, and M. Goto,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCreating Learning Environments through eco-design,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">North American Association for Environmental Education, 34th Annual Conference, Oct. 2005.<\/span>\r\n          <\/li>\r\n        <\/ol>\r\n      <\/details>\r\n\r\n      <details class=\"ml-pubdetails\" id=\"presentations\">\r\n        <summary>\u56fd\u5185\u5b66\u4f1a\u30fb\u7814\u7a76\u4f1a<span>156\u4ef6<\/span><\/summary>\r\n        <ol>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u52a0\u6cbb\u5c4b\u76f4\u5e0c\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cTAO\u306b\u57fa\u3065\u304f\u7570\u5e38\u691c\u77e5\u30e2\u30c7\u30eb\u306e\u8aac\u660e\u53ef\u80fd\u6027\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2026\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2026\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9ad8\u6a4b\u6696\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cHRBM\u306b\u5bfe\u3059\u308b\u80fd\u52d5\u5b66\u7fd2\u306e\u9069\u7528\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2026\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2026\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u7a17\u7530\u5929\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c5e\u6027\u60c5\u5831\u4ed8\u304d\u95a2\u4fc2\u30c7\u30fc\u30bf\u5206\u6790\u306e\u305f\u3081\u306e\u6307\u6570\u578b\u5206\u5e03\u65cf\u306b\u57fa\u3065\u304f\u6f5c\u5728\u69cb\u9020\u30e2\u30c7\u30eb\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2026\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2026\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5bae\u5ddd\u5feb\u571f\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cRAG-Fusion\u3092\u57fa\u306b\u3057\u305fLLM\u306b\u304a\u3051\u308b\u30cf\u30eb\u30b7\u30cd\u30fc\u30b7\u30e7\u30f3\u6539\u5584\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a2026\u5e74\u7dcf\u5408\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2026\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5869\u5c3b\u5927\u7fd4\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCollaborative Metric Learning\u306b\u3088\u308b\u516c\u5e73\u6027\u3092\u8003\u616e\u3057\u305f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2025\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2025\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5e73\u5c71\u5bdb\u6c17\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cActive Adversarial Domain Adaptation\u306b\u304a\u3051\u308b\u6700\u9069\u8f38\u9001\u8a08\u753b\u306e\u5206\u5e03\u4e0d\u6574\u5408\u3092\u8003\u616e\u3057\u305f\u62e1\u5f35\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2025\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2025\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u962a\u4e95\u512a\u592a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cECOC\u6cd5\u3092\u6d3b\u7528\u3057\u305f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3088\u308b\u8aac\u660e\u53ef\u80fd\u591a\u5024\u5206\u985e\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">2025\u5e74\u5ea6\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2025\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c71\u4e0b\u9065\uff0c\u9f4a\u85e4\u53f2\u54f2\uff0c\u7530\u7551\u667a\u7ae0\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u95a2\u5eb8\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7d4c\u55b6\u5de5\u5b66\u6559\u80b2\u306b\u304a\u3051\u308b\u751f\u6210AI\u306e\u30a4\u30f3\u30d1\u30af\u30c8\u3068\u5c06\u6765\u5c55\u671b\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2025\u5e74\u79cb\u5b63\u5927\u4f1a\uff0c2025\u5e7410\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e2d\u5cf6\u60a0\u6597\uff0c\u85e4\u539f\u8ce2\u4e8c\uff0c\u5409\u7530\u5247\u88d5\uff0c\u6238\u7530\u822a\u53f2\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4e8b\u524d\u5b66\u7fd2\u6e08\u307f\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u8ee2\u79fb\u5b66\u7fd2\u306b\u3088\u308bExtract Method\u30ea\u30d5\u30a1\u30af\u30bf\u30ea\u30f3\u30b0\u4e88\u6e2c\u306e\u7cbe\u5ea6\u5411\u4e0a\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c31\u56de\u30bd\u30d5\u30c8\u30a6\u30a7\u30a2\u5de5\u5b66\u306e\u57fa\u790e\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\uff0c2024\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u798f\u7530\u7adc\u4e5f\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cSemi-supervised signed stochastic block model\u306e\u8907\u6570\u30c9\u30e1\u30a4\u30f3\u3078\u306e\u62e1\u5f35\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2024\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2024\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6709\u99ac\u7965\u592a\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6642\u7cfb\u5217\u95a2\u4fc2\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308bStochastic Block Model\u306e\u62e1\u5f35\u3068\u8cfc\u8cb7\u30c7\u30fc\u30bf\u3078\u306e\u9069\u7528\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2024\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2024\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f8c\u85e4\u6b63\u5e78\uff0c\u5b88\u53e3\u525b\uff0c\u95a2\u5eb8\u4e00\uff0c\u9234\u6728\u79c0\u7537\uff0c\u751f\u7530\u76ee\u5d07\uff0c\u4e2d\u7530\u548c\u79c0\uff0c\u77f3\u57a3\u7dbe\uff0c\u4e0a\u7530\u96c5\u592b\uff0c\u4f50\u85e4\u516c\u4fca\uff0c\u7530\u7551\u667a\u7ae0\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c71\u4e0b\u9065\uff0c\u694a\u6dfb\u7fd4\uff0c\u5c71\u6975\u7dbe\u5b50\uff0c\u962a\u4e95\u512a\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u8c37\u53e3\u5353\u4e5f\uff0c\u5409\u7530\u7384\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u53cd\u8ee2\u5f62\u5f0f\u306e\u591a\u5927\u5b66\u9023\u643a\u578b\u30ab\u30f3\u30d5\u30a1\u30ec\u30f3\u30b9\u306b\u57fa\u3065\u304f\u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u6559\u80b2\u30e2\u30c7\u30eb\u3068\u305d\u306e\u52b9\u679c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u6559\u80b2\u5de5\u5b66\u4f1a2024\u5e74\u79cb\u5b63\u5168\u56fd\u5927\u4f1a\uff0c2024\u5e749\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u962a\u4e95\u512a\u592a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u89d2\u5ea6\u306b\u57fa\u3065\u3044\u305f\u9ad8\u6b21\u5143\u30c7\u30fc\u30bf\u53ef\u8996\u5316\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c46\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2023\uff09\u4e88\u7a3f\u96c6\uff0c2023\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u798f\u7530\u7adc\u4e5f\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4e0d\u5747\u8861\u30c7\u30fc\u30bf\u306b\u5bfe\u3059\u308bMDO\u306e\u7cbe\u5ea6\u5411\u4e0a\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2023\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2023\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u77f3\u5ddd\u60a0\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u4e8c\u5bae\u6d0b\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCorrect and Smooth\u3092\u7528\u3044\u305f\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u7b2c85\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2023\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u6fa4\u52c7\u7fd4\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6bcd\u89aa\u5411\u3051\u60c5\u5831\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u30d9\u30b9\u30c8\u30a2\u30f3\u30b5\u30fc\u4e88\u6e2c\u3068\u91cd\u8981\u8cea\u554f\u62bd\u51fa\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2022\u5e74\u6625\u5b63\u5927\u4f1a\uff0c2022\u5e745\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c5e\u6027\u60c5\u5831\u4ed8\u304d\u95a2\u4fc2\u30c7\u30fc\u30bf\u3092\u5bfe\u8c61\u3068\u3057\u305f\u6f5c\u5728\u69cb\u9020\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2022\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2022\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u77f3\u5ddd\u60a0\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCorrect and Smooth\u306e\u5206\u985e\u554f\u984c\u3078\u306e\u9069\u7528\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2022\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2022\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6ff1\u7530\u8056\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cECOC\u6cd5\u3092\u7528\u3044\u305fSNMF\u306b\u3088\u308b\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2022\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2022\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f8c\u85e4\u6b63\u5e78\uff0c\u5c0f\u6797\u5b66\uff0c\u5b88\u53e3\u525b\uff0c\u95a2\u5eb8\u4e00\uff0c\u9234\u6728\u79c0\u7537\uff0c\u751f\u7530\u76ee\u5d07\uff0c\u4e2d\u7530\u548c\u79c0\uff0c\u77f3\u57a3\u7dbe\uff0c\u4e0a\u7530\u96c5\u592b\uff0c\u4f50\u85e4\u516c\u4fca\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c71\u4e0b\u9065\uff0c\u7530\u5c3b\u88d5\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u591a\u5927\u5b66\u9023\u643a\u578b\u30aa\u30f3\u30e9\u30a4\u30f3\u53cd\u8ee2\u30bc\u30df\u306e\u8a2d\u8a08\u3068\u305d\u306e\u6559\u80b2\u52b9\u679c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c21\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\uff08FIT2022\uff09\uff0c2022\u5e749\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f8c\u85e4\u6b63\u5e78\uff0c\u5c0f\u6797\u5b66\uff0c\u5b88\u53e3\u525b\uff0c\u95a2\u5eb8\u4e00\uff0c\u9234\u6728\u79c0\u7537\uff0c\u751f\u7530\u76ee\u5d07\uff0c\u4e2d\u7530\u548c\u79c0\uff0c\u77f3\u57a3\u7dbe\uff0c\u4e0a\u7530\u96c5\u592b\uff0c\u4f50\u85e4\u516c\u4fca\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c71\u4e0b\u9065\uff0c\u7530\u5c3b\u88d5\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u53cd\u8ee2\u30bc\u30df\u5f62\u5f0f\u306b\u3088\u308b\u591a\u5927\u5b66\u3067\u9023\u643a\u3059\u308b\u30aa\u30f3\u30e9\u30a4\u30f3\u7814\u7a76\u4ea4\u6d41\u306e\u8a66\u307f \u2015 \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u9818\u57df\u306e\u30aa\u30f3\u30e9\u30a4\u30f3\u30bc\u30df\u3092\u4e8b\u4f8b\u3068\u3057\u3066 \u2015\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">2022 PC\u30ab\u30f3\u30d5\u30a1\u30ec\u30f3\u30b9\uff0c2022\u5e748\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6ff1\u7530\u8056\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21132\u6b63\u5247\u5316\u3092\u7528\u3044\u305fSNMF\u306b\u3088\u308b\u591a\u5024\u6587\u66f8\u5206\u985e\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2021\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5186\u57ce\u5bfa\u601c\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30aa\u30f3\u30e9\u30a4\u30f3\u578bFactorization Machine\u306e\u591a\u5024\u5206\u985e\u3078\u306e\u62e1\u5f35\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2021\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u4fa1\u683c\u4fdd\u6301\u671f\u9593\u306e\u9069\u6b63\u5316\u30e2\u30c7\u30eb\u3068\u5b9f\u8a3c\u7684\u52b9\u679c\u691c\u8a3c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c19\u56de\u65e5\u672c\u30c7\u30fc\u30bf\u30d9\u30fc\u30b9\u5b66\u4f1a\u5e74\u6b21\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e1\u30c8\u30ea\u30c3\u30af\u30e9\u30fc\u30cb\u30f3\u30b0\u306b\u304a\u3051\u308b\u6f5c\u5728\u69cb\u9020\u30e2\u30c7\u30eb\u3068\u6f5c\u5728\u5909\u6570\u306e\u63a8\u5b9a\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2021\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6ff1\u7530\u8056\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6559\u5e2b\u3042\u308a\u975e\u8ca0\u5024\u884c\u5217\u56e0\u5b50\u5206\u89e3\u3092\u7528\u3044\u305f\u30c6\u30ad\u30b9\u30c8\u30c7\u30fc\u30bf\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2021\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5186\u57ce\u5bfa\u601c\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21131\u6b63\u5247\u5316\u3092\u7528\u3044\u305f\u30aa\u30f3\u30e9\u30a4\u30f3\u578bFactorization Machine\u3092\u7528\u3044\u305f\u6587\u66f8\u5206\u985e\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2021\u5e74\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2021\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4ee3\u8868\u5143\u3092\u7528\u3044\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u304a\u3051\u308b\u30aa\u30f3\u30e9\u30a4\u30f3\u5b66\u7fd2\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2020\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u671d\u898b\u7965\u592a\u90ce\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21131\u6b63\u5247\u5316\u3092\u7528\u3044\u305f\u30aa\u30f3\u30e9\u30a4\u30f3\u578bFactorization Machine\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2020\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9234\u6728\u7f8e\u6ce2\uff0c\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6559\u5e2b\u3042\u308a\u975e\u8ca0\u5024\u884c\u5217\u56e0\u5b50\u5206\u89e3\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u66f4\u65b0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2020\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9752\u6728\u7ae0\u609f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30bf\u30fc\u30b2\u30c3\u30c8\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306e\u305f\u3081\u306eWeb\u95b2\u89a7\u884c\u52d5\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u5c5e\u6027\u9078\u629e\u3068\u305d\u306e\u8a55\u4fa1\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a2020\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9752\u6728\u7ae0\u609f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30bf\u30fc\u30b2\u30c3\u30c8\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306e\u305f\u3081\u306eWeb\u95b2\u89a7\u5c65\u6b74\u306b\u3088\u308b\u5c5e\u6027\u30e9\u30d9\u30eb\u5b66\u7fd2\u30e2\u30c7\u30eb\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u8a08\u7b97\u6a5f\u7d71\u8a08\u5b66\u4f1a\u7b2c34\u56de\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u962a\u4e95\u512a\u592a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c5e\u6027\u60c5\u5831\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u8003\u616e\u3057\u305f\u30bc\u30ed\u30b7\u30e7\u30c3\u30c8\u751f\u6210\u30e2\u30c7\u30eb\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c23\u56de\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\uff08IBIS2020\uff09\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u4f50\u3005\u6728\u5317\u90fd\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u4fa1\u683c\u4fdd\u6301\u671f\u9593\u306e\u9069\u6b63\u5316\u30e2\u30c7\u30eb\u3068\u5b9f\u8a3c\u7684\u52b9\u679c\u691c\u8a3c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c19\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\uff08FIT2020\uff09\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u91d1\u6fa4\u771f\u5e73\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u4f50\u3005\u6728\u5317\u90fd\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6a5f\u68b0\u5b66\u7fd2\u30a2\u30d7\u30ed\u30fc\u30c1\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u4fa1\u683c\u4fdd\u6301\u671f\u9593\u306e\u9069\u6b63\u5316\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c34\u56de\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08JSAI2020\uff09\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e0a\u539f\u8ad2\u4ecb\uff0c\u677e\u5143\u7422\u771f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30af\u30e9\u30b9\u30de\u30eb\u30b3\u30d5\u9023\u9396\u306b\u3088\u308b\u30d7\u30ed\u91ce\u7403\u5148\u767a\u6295\u624b\u306e\u5931\u70b9\u4e88\u6e2c\u30e2\u30c7\u30ea\u30f3\u30b0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c34\u56de\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08JSAI2020\uff09\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e05\u6c34\u745b\u8cb4\uff0c\u9752\u6728\u7ae0\u609f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u5909\u6570\u3092\u5c0e\u5165\u3057\u305f\u30d1\u30e9\u30e1\u30fc\u30bf\u8abf\u6574\u306b\u57fa\u3065\u304f\u30e1\u30bf\u5b66\u7fd2\u6cd5\u306e\u6539\u826f\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c34\u56de\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u5168\u56fd\u5927\u4f1a\uff08JSAI2020\uff09\u4e88\u7a3f\u96c6\uff0c2020\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6749\u5d0e\u667a\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c7\u30fc\u30bf\u306e\u6f5c\u5728\u7684\u7279\u5fb4\u3092\u8003\u616e\u3057\u305fFactorization Machines\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c42\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2019\uff09\u4e88\u7a3f\u96c6\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u4e95\u4e00\u8cb4\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u81a8\u5927\u306a\u7a2e\u985e\u306e\u30a2\u30a4\u30c6\u30e0\u3092\u8003\u616e\u3057\u305f\u6d88\u8cbb\u8005\u8cfc\u8cb7\u884c\u52d5\u306e\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c42\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2019\uff09\u4e88\u7a3f\u96c6\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9752\u6728\u7ae0\u609f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cWeb\u95b2\u89a7\u5c65\u6b74\u306b\u57fa\u3065\u304f\u30e6\u30fc\u30b6\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u304a\u3051\u308b\u5404\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u6319\u52d5\u5206\u6790\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c22\u56de\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\uff08IBIS2019\uff09\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u83ab\u921e\u8cbd\uff0c\u85e4\u6ce2\u82f1\u8f1d\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e9\u30f3\u30af\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u6599\u7406\u753b\u50cf\u306e\u9b45\u529b\u5ea6\u5b9a\u91cf\u5316\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c22\u56de\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u30ef\u30fc\u30af\u30b7\u30e7\u30c3\u30d7\uff08IBIS2019\uff09\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6851\u7530\u548c\uff0c\u6749\u5d0e\u667a\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8ca9\u58f2\u5c65\u6b74\u30c7\u30fc\u30bf\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u51fa\u54c1\u4fa1\u683c\u63a8\u5b9a\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c33\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u962a\u4e95\u512a\u592a\uff0c\u5b89\u4e95\u4e00\u8cb4\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u534a\u6559\u5e2b\u3042\u308a\u30d6\u30fc\u30b9\u30c6\u30a3\u30f3\u30b0\u306e\u591a\u5024\u5206\u985e\u3078\u306e\u62e1\u5f35\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u4eba\u5de5\u77e5\u80fd\u5b66\u4f1a\u7b2c33\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2019\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u7560\u5c71\u4e00\u8f1d\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cDQN\u3092\u7528\u3044\u305fMineCraft\u306b\u304a\u3051\u308b\u69cb\u9020\u7269\u306e\u81ea\u52d5\u69cb\u7bc9\u306e\u691c\u8a0e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a2019\u5e74\u7dcf\u5408\u5927\u4f1a\uff0cD-8-10\uff0c2019\u5e743\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u4e95\u4e00\u8cb4\uff0c\u4e2d\u91ce\u4fee\u5e73\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5468\u671f\u6027\u3068\u30a4\u30d9\u30f3\u30c8\u52b9\u679c\u306b\u7740\u76ee\u3057\u305f\u6d88\u8cbb\u8005\u306e\u8cfc\u8cb7\u884c\u52d5\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621030\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2018\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6749\u5d0e\u667a\u54c9\uff0c\u897f\u5c3e\u53cb\u91cc\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u685c\u4e95\u5d07\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7279\u5fb4\u9593\u306e\u4ea4\u4e92\u4f5c\u7528\u3092\u8003\u616e\u3057\u305f\u5b66\u751f\u30e6\u30fc\u30b6\u306e\u4f01\u696d\u30a8\u30f3\u30c8\u30ea\u30fc\u884c\u52d5\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621030\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2018\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21131\u6b63\u5247\u5316\u306b\u57fa\u3065\u304fFactorization Machine\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621030\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2018\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u7560\u5c71\u4e00\u8f1d\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cMineCraft\u3092\u7528\u3044\u305fDQN\u306b\u3088\u308b\u69cb\u9020\u7269\u306e\u81ea\u52d5\u69cb\u7bc9\u306e\u691c\u8a0e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">2018\u5e74\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u30bd\u30b5\u30a4\u30a8\u30c6\u30a3\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2018\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e2d\u91ce\u4fee\u5e73\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCanonical Correlation Forests\u306b\u304a\u3051\u308b\u30e9\u30d9\u30eb\u884c\u5217\u306e\u30b9\u30d1\u30fc\u30b9\u6027\u3092\u8003\u616e\u3057\u305f\u5206\u985e\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u7b2c121\u56de\u6570\u7406\u30e2\u30c7\u30eb\u5316\u3068\u554f\u984c\u89e3\u6c7a\u7814\u7a76\u767a\u8868\u4f1a\uff0c2018\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u696d\u5929\u5927\u8cb4\uff0c\u4ec1\u30ce\u5e73\u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c40\u6240\u7684\u69cb\u9020\u306b\u7740\u76ee\u3057\u305f\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u306b\u95a2\u3059\u308b\u7814\u7a76\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621029\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2017\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5ca1\u5c71\u6210\uff0c\u5c71\u4e0b\u9065\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u975e\u8ca0\u5024\u884c\u5217\u56e0\u5b50\u5206\u89e3\u306b\u57fa\u3065\u304f\u6c17\u8c61\u6761\u4ef6\u3068\u5546\u54c1\u58f2\u4e0a\u30d1\u30bf\u30fc\u30f3\u306e\u95a2\u4fc2\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u7b2c79\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2017\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c0f\u6797\u5b66\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306b\u3088\u308b\u975e\u8ca0\u5024\u884c\u5217\u56e0\u5b50\u5206\u89e3\u306e\u8868\u73fe\u3068\u305d\u306e\u62e1\u5f35\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u7b2c79\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2017\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u8352\u4e95\u7422\u5145\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u5206\u5e03\u306b\u304a\u3051\u308b\u5c64\u5225\u5909\u6570\u304c\u3042\u308b\u5834\u5408\u306e\u30d9\u30a4\u30ba\u6700\u9069\u306a\u4e88\u6e2c\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u8a08\u7b97\u6a5f\u7d71\u8a08\u5b66\u4f1a\u7b2c31\u56de\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4ec1\u30ce\u5e73\u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u8ca9\u58f2\u4fa1\u683c\u4e88\u6e2c\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u4fa1\u683c\u8a2d\u5b9a\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u8a08\u7b97\u6a5f\u7d71\u8a08\u5b66\u4f1a\u7b2c31\u56de\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u8352\u4e95\u7422\u5145\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30ed\u30b8\u30b9\u30c6\u30a3\u30c3\u30af\u56de\u5e30\u30e2\u30c7\u30eb\u65cf\u306b\u5bfe\u3059\u308b\u30d9\u30a4\u30ba\u6700\u9069\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c40\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2017\uff09\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5ca1\u5c71\u6210\uff0c\u5c71\u4e0b\u9065\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u975e\u8ca0\u5024\u30c6\u30f3\u30bd\u30eb\u56e0\u5b50\u5206\u89e3\u306b\u57fa\u3065\u304f\u6c17\u8c61\u6761\u4ef6\u3068\u8cfc\u8cb7\u30d1\u30bf\u30fc\u30f3\u306e\u95a2\u4fc2\u89e3\u6790\u30e2\u30c7\u30eb\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c40\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2017\uff09\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4ec1\u30ce\u5e73\u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6df7\u5408\u56de\u5e30\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u4e2d\u53e4\u30d5\u30a1\u30c3\u30b7\u30e7\u30f3\u30a2\u30a4\u30c6\u30e0\u306e\u8ca9\u58f2\u4fa1\u683c\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c40\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2017\uff09\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u516b\u6728\u79c0\u6a39\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4eba\u5de5\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u8aa4\u308a\u8a02\u6b63\u7b26\u53f7\u306b\u57fa\u3065\u304f\u591a\u5024\u5206\u985e\u6cd5\u306b\u304a\u3051\u308b\u7b26\u53f7\u8a9e\u8868\u69cb\u6210\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c40\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2017\uff09\uff0c2017\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">M. Kobayashi, K. Mikawa, M. Goto, and S. Hirasawa,<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cCollaborative Filtering Based on the Latent Class Model Using Variational Bayes,\u201d<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c40\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2017\uff09, Nov. 2017.<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u99ac\u8cc0\u5d69\u58eb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6c17\u8c61\u60c5\u5831\u3068Tweet\u30c7\u30fc\u30bf\u306e\u7d71\u5408\u7684\u5206\u6790\u306b\u3088\u308b\u4f53\u611f\u6c17\u6e29\u306e\u5b9a\u91cf\u5316\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c15\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u672c\u7950\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u306b\u3088\u308b\u6587\u66f8\u63a8\u85a6\u624b\u6cd5\u306b\u3064\u3044\u3066\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c15\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u677e\u5d5c\u7950\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u9867\u5ba2\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u3092\u76ee\u7684\u3068\u3057\u305f\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8cfc\u8cb7\u884c\u52d5\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c15\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f35\u5029\uff0c\u5c71\u4e0b\u9065\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u521d\u671f\u8cfc\u8cb7\u3092\u8003\u616e\u3057\u305fRFM\u5206\u6790\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c15\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u8352\u4e95\u7422\u5145\uff0c\u5c71\u672c\u7950\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u5b66\u7fd2\u306b\u57fa\u3065\u304f\u8fd1\u4f3c\u30d9\u30a4\u30ba\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e2d\u91ce\u4fee\u5e73\uff0c\u6c38\u68ee\u8aa0\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c40\u6240\u7684\u8ddd\u96e2\u5b66\u7fd2\u3068\u9069\u5fdc\u7684\u91cd\u307f\u4ed8\u3051\u548c\u306b\u57fa\u3065\u304fk\u6700\u8fd1\u508d\u6cd5\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u99ac\u8cc0\u5d69\u58eb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6c17\u8c61\u60c5\u5831\u3068Tweet\u30c7\u30fc\u30bf\u306e\u7d71\u5408\u7684\u5206\u6790\u306b\u3088\u308b\u4f53\u611f\u6c17\u6e29\u306e\u5b9a\u91cf\u5316\u3068\u305d\u306e\u9700\u8981\u4e88\u6e2c\u3078\u306e\u5229\u7528\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c39\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u672c\u7950\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLDA\u306b\u57fa\u3065\u304f\u672a\u89b3\u6e2c\u306a\u30ab\u30c6\u30b4\u30ea\u3092\u542b\u3080\u6587\u66f8\u96c6\u5408\u306e\u81ea\u52d5\u5206\u985e\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c39\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u677e\u5d5c\u7950\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cEC\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u65bd\u7b56\u52b9\u679c\u5411\u4e0a\u3092\u76ee\u7684\u3068\u3057\u305f\u30de\u30eb\u30b3\u30d5\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c39\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u99ac\u8cc0\u5d69\u58eb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6c17\u8c61\u60c5\u5831\u3068Tweet\u30c7\u30fc\u30bf\u306e\u7d71\u5408\u5206\u6790\u306b\u57fa\u3065\u304f\u4f53\u611f\u6c17\u6e29\u306e\u5b9a\u91cf\u5316\u306b\u3064\u3044\u3066\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u672c\u7950\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c8\u30d4\u30c3\u30af\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u672a\u89b3\u6e2c\u30ab\u30c6\u30b4\u30ea\u3092\u542b\u3080\u6587\u66f8\u96c6\u5408\u306e\u81ea\u52d5\u5206\u985e\u306b\u3064\u3044\u3066\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u677e\u5d5c\u7950\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u65bd\u7b56\u52b9\u679c\u306e\u6700\u9069\u5316\u3092\u76ee\u7684\u3068\u3057\u305f\u30de\u30eb\u30b3\u30d5\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u3088\u308b\u8cfc\u8cb7\u884c\u52d5\u5206\u6790\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f8c\u85e4\u6b63\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5c71\u4e0b\u9065\uff0c\u5409\u958b\u670b\u5f18\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cTweet\u30c7\u30fc\u30bf\u3092\u5bfe\u8c61\u3068\u3057\u305fWord2Vec\u306b\u3088\u308b\u5546\u54c1\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621028\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f8c\u85e4\u6b63\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u5c0f\u6797\u5b66\uff0c\u8352\u672c\u9053\u9686\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7de8\u96c6\u5c65\u6b74\u53ef\u8996\u5316\u30b7\u30b9\u30c6\u30e0\u3092\u7528\u3044\u305fLearning Analytics \uff5eC\u30d7\u30ed\u30b0\u30e9\u30df\u30f3\u30b0\u79d1\u76ee\u306b\u304a\u3051\u308b\u7de8\u96c6\u5c65\u6b74\u3068\u8a55\u4fa1\u5f97\u70b9\u30c7\u30fc\u30bf\u3092\u7d71\u5408\u3057\u305f\u5206\u6790\u30e2\u30c7\u30eb\uff5e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u7b2c78\u56de\u5168\u56fd\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2016\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9152\u4e95\u62d3\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u96e2\u53cd\u9867\u5ba2\u767a\u898b\u3092\u76ee\u7684\u3068\u3059\u308b\u5224\u5225\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5f35\u5029\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8cfc\u8cb7\u60c5\u5831\u3092\u7528\u3044\u305fRFM\u5206\u6790\u306b\u57fa\u3065\u304f\u9867\u5ba2\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u672c\u7950\u751f\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30c8\u30d4\u30c3\u30af\u3092\u8003\u616e\u3057\u305f\u672a\u89b3\u6e2c\u30ab\u30c6\u30b4\u30ea\u3092\u542b\u3080\u6587\u66f8\u96c6\u5408\u306e\u81ea\u52d5\u5206\u985e\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u91ce\u6d25\u7422\u767b\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u88ab\u30a8\u30f3\u30c8\u30ea\u6570\u306e\u4e88\u6e2c\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9234\u6728\u73b2\u592e\u5948\uff0c\u5c71\u4e0a\u6562\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c3\u5143\u76f4\u4ea4\u8868\u306b\u57fa\u3065\u304fECOC\u6cd5\u306b\u3088\u308b\u591a\u5024\u6587\u66f8\u5206\u985e\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u99ac\u8cc0\u5d69\u58eb\uff0c\u6e6f\u5ddd\u8f1d\u4e00\u6717\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cInformation-Theoretic Metric Learning\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u3092\u76ee\u7684\u3068\u3057\u305f\u5b66\u7fd2\u30c7\u30fc\u30bf\u30da\u30a2\u306e\u9078\u629e\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u677e\u5d5c\u7950\u6a39\uff0c\u5c71\u4e0a\u6562\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30af\u30fc\u30dd\u30f3\u4ed8\u8cfc\u8cb7\u5c65\u6b74\u30c7\u30fc\u30bf\u3092\u7528\u3044\u305f\u9867\u5ba2\u8cfc\u8cb7\u884c\u52d5\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6c38\u68ee\u8aa0\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30a8\u30f3\u30c8\u30ea\u50be\u5411\u306e\u6642\u9593\u7684\u5909\u5316\u3092\u8003\u616e\u3057\u305f\u30e6\u30fc\u30b6\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u57fa\u3065\u304f\u5c31\u8077\u6d3b\u52d5\u7d42\u4e86\u6642\u671f\u4e88\u6e2c\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u6625\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9f4b\u85e4\u6d0b\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8907\u6570\u306e\u5c40\u6240\u8ddd\u96e2\u69cb\u9020\u3092\u8003\u616e\u3057\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u91ce\u6d25\u7422\u767b\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u88ab\u30a8\u30f3\u30c8\u30ea\u6570\u306e\u4e88\u6e2c\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u7530\u76f4\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cECOC\u591a\u5024\u5224\u5225\u624b\u6cd5\u306b\u5bfe\u3059\u308b\u30b3\u30b9\u30c8\u8003\u616e\u578b\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c38\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u963f\u5185\u5b8f\u6b66\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e2\u30c7\u30eb\u30af\u30e9\u30b9\u3092\u62e1\u5f35\u3057\u305f\u5834\u5408\u306e\u30d9\u30a4\u30ba\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c38\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u4e0a\u6562\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u5b66\u751f\u306e\u5c31\u8077\u6d3b\u52d5\u7d42\u4e86\u65e5\u4e88\u6e2c\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c38\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e6f\u5ddd\u8f1d\u4e00\u6717\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8a08\u7b97\u91cf\u306e\u524a\u6e1b\u3092\u76ee\u7684\u3068\u3057\u305f\u5206\u6563\u578bSVM\u306e\u5b66\u7fd2\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u694a\u6dfb\u7fd4\uff0c\u677f\u57a3\u76f4\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30d9\u30fc\u30bf\u5206\u5e03\u3092\u5c0e\u5165\u3057\u305fpLSA\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u4e0a\u6562\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5b66\u751f\u306e\u5c5e\u6027\u60c5\u5831\u3068\u884c\u52d5\u5c65\u6b74\u60c5\u5831\u3092\u7528\u3044\u305f\u5c31\u8077\u6d3b\u52d5\u7d42\u4e86\u65e5\u4e88\u6e2c\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u963f\u5185\u5b8f\u6b66\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u751f\u8d77\u9806\u5e8f\u3068\u7d2f\u7a4d\u751f\u8d77\u56de\u6570\u304c\u6df7\u5728\u3059\u308b\u6761\u4ef6\u4ed8\u304d\u78ba\u7387\u30e2\u30c7\u30eb\u306b\u5bfe\u3059\u308b\u30d9\u30a4\u30ba\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4ee3\u8868\u5143\u9593\u306e\u8ddd\u96e2\u69cb\u9020\u3092\u7528\u3044\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u91ce\u6d25\u7422\u767b\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u837b\u539f\u5927\u9678\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u5404\u4f01\u696d\u306e\u88ab\u30a8\u30f3\u30c8\u30ea\u6570\u306e\u4e88\u6e2c\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9f4b\u85e4\u6d0b\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c40\u6240\u7684\u69cb\u9020\u3092\u30e2\u30c7\u30eb\u5316\u53ef\u80fd\u306a\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u7530\u76f4\u751f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u591a\u5024\u5224\u5225\u554f\u984c\u306b\u304a\u3051\u308b\u30b3\u30b9\u30c8\u8003\u616e\u578b\u5b66\u7fd2\u3078\u306e\u62e1\u5f35\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u6ca2\u7fd4\u592a\u90ce\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6c4e\u5316\u6027\u80fd\u3092\u8003\u616e\u3057\u305fAlternating Decision Forests\u306e\u6539\u826f\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621027\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u6ca2\u7fd4\u592a\u90ce\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30d6\u30fc\u30c8\u30b9\u30c8\u30e9\u30c3\u30d7\u6cd5\u3092\u7528\u3044\u305fAlternating Decision Forests\u306e\u9069\u5fdc\u7684\u306a\u6c4e\u5316\u6027\u80fd\u5411\u4e0a\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e6f\u5ddd\u8f1d\u4e00\u6717\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c7\u30fc\u30bf\u306e\u8ee2\u9001\u5236\u5fa1\u306b\u57fa\u3065\u304f\u52b9\u7387\u7684\u306a\u5206\u6563\u578bSVM\u306e\u5b66\u7fd2\u624b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2015\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u6ca2\u7fd4\u592a\u90ce\uff0c\u85e4\u539f\u76f4\u5e83\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u88ab\u4e88\u6e2c\u6027\u80fd\u306b\u7740\u76ee\u3057\u305fAlternating Decision Forests\u306e\u5404\u6c7a\u5b9a\u6728\u3078\u306e\u91cd\u307f\u4ed8\u3051\u4e88\u6e2c\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1aIT\u7814\u7a76\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9f4b\u85e4\u6d0b\uff0c\u5c71\u5d0e\u53f2\u535a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4f4e\u6b21\u5143\u8a08\u91cf\u884c\u5217\u306e\u5b66\u7fd2\u3068\u305d\u306e\u7d50\u5408\u306b\u3088\u308b\u8a08\u91cf\u884c\u5217\u5b66\u7fd2\u306e\u8a08\u7b97\u91cf\u524a\u6e1b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1aIT\u7814\u7a76\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4ee3\u8868\u5143\u9593\u306e\u8ddd\u96e2\u69cb\u9020\u306b\u7740\u76ee\u3057\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u5d0e\u53f2\u535a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLarge Margin Nearest Neighbor\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u3092\u76ee\u7684\u3068\u3057\u305f\u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u91cd\u307f\u4ed8\u3051\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u963f\u5185\u5b8f\u6b66\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30b7\u30f3\u30dc\u30eb\u306e\u7d2f\u7a4d\u51fa\u73fe\u56de\u6570\u3092\u6761\u4ef6\u3068\u3059\u308b\u30e2\u30c7\u30eb\u30af\u30e9\u30b9\u306e\u30d9\u30a4\u30ba\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u3064\u3044\u3066\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u85e4\u539f\u76f4\u5e83\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u95b2\u89a7\u53ca\u3073\u8cfc\u8cb7\u884c\u52d5\u3092\u540c\u6642\u306b\u8868\u73fe\u3059\u308b\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u3068\u305d\u306e\u5b66\u7fd2\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u79cb\u5c71\u9f8d\u592a\u90ce\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u985e\u4f3c\u6027\u306b\u57fa\u3065\u304f\u30e9\u30d9\u30eb\u9078\u629e\u6cd5\u3092\u7528\u3044\u305f\u30de\u30eb\u30c1\u30c8\u30d4\u30c3\u30af\u6587\u66f8\u5206\u985e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u53e4\u5c71\u4eae\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u6f5c\u5728\u30af\u30e9\u30b9\u3092\u7528\u3044\u305f\u30ec\u30b3\u30e1\u30f3\u30c9\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u7814\u7a76\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30ab\u30c6\u30b4\u30ea\u306e\u7d71\u8a08\u7684\u7279\u5fb4\u3092\u5229\u7528\u3057\u305f\u9069\u5fdc\u7684\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621026\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u963f\u5185\u5b8f\u6b66\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30b7\u30f3\u30dc\u30eb\u306e\u7d2f\u7a4d\u51fa\u73fe\u56de\u6570\u3092\u8003\u616e\u3057\u305f\u30d9\u30a4\u30ba\u4e88\u6e2c\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621026\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u53e4\u5c71\u4eae\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u6f5c\u5728\u30af\u30e9\u30b9\u3092\u7528\u3044\u305f\u30ec\u30b3\u30e1\u30f3\u30c9\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u7814\u7a76\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621026\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5b89\u7530\u76f4\u751f\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cECOC SVM\u306b\u304a\u3051\u308b\u30c7\u30fc\u30bf\u6570\u306e\u504f\u308a\u3092\u8003\u616e\u3057\u305f\u591a\u5024\u6587\u66f8\u5206\u985e\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621026\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u4e0a\u6562\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u6839\u53f7\u65e5\u306b\u7740\u76ee\u3057\u305f\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621026\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u5d0e\u53f2\u535a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLarge Margin Nearest Neighbor\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u3092\u76ee\u7684\u3068\u3057\u305f\u5b66\u7fd2\u30c7\u30fc\u30bf\u306e\u91cd\u307f\u3065\u3051\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u90e8\u9580\u5b66\u8853\u8b1b\u6f14\u4f1a2014\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u85e4\u539f\u76f4\u5e83\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u95b2\u89a7\u53ca\u3073\u8cfc\u8cb7\u884c\u52d5\u3092\u540c\u6642\u306b\u5b66\u7fd2\u53ef\u80fd\u306a\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u90e8\u9580\u5b66\u8853\u8b1b\u6f14\u4f1a2014\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9f4b\u85e4\u6d0b\uff0c\u5c71\u5d0e\u53f2\u535a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4f4e\u6b21\u5143\u8a08\u91cf\u884c\u5217\u306e\u5b66\u7fd2\u3068\u305d\u306e\u7d50\u5408\u306b\u3088\u308b\u8a08\u91cf\u884c\u5217\u5b66\u7fd2\u306e\u8a08\u7b97\u91cf\u524a\u6e1b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u8a08\u6e2c\u81ea\u52d5\u5236\u5fa1\u5b66\u4f1a\u30b7\u30b9\u30c6\u30e0\u60c5\u5831\u90e8\u9580\u5b66\u8853\u8b1b\u6f14\u4f1a2014\u4e88\u7a3f\u96c6\uff0c2014\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u53e4\u5c71\u4eae\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u6f5c\u5728\u30af\u30e9\u30b9\u3092\u7528\u3044\u305f\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c37\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2014\uff09\uff0c2014\u5e7412\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e6f\u5ddd\u8f1d\u4e00\u6717\uff0c\u837b\u539f\u5927\u9678\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4e09\u8005\u4ee5\u4e0a\u3067\u30d7\u30e9\u30a4\u30d0\u30b7\u30fc\u3092\u4fdd\u8b77\u3059\u308b\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u5206\u6563\u8a08\u7b97\u6cd5\u306b\u3064\u3044\u3066\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621025\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c71\u5d0e\u53f2\u535a\uff0c\u5cef\u82eb\u548c\u53f2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cLarge Margin Nearest Neighbor\u306e\u5206\u985e\u7cbe\u5ea6\u5411\u4e0a\u3092\u76ee\u7684\u3068\u3057\u305f\u5b66\u7fd2\u30c7\u30fc\u30bf\u9078\u629e\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621025\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21131\u6b63\u5247\u5316\u3092\u7528\u3044\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u3088\u308b\u7279\u5fb4\u9078\u629e\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621025\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u674e\u6607\u70af\uff0c\u5927\u68ee\u60a0\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e6\u30fc\u30b6\u30ec\u30d3\u30e5\u30fc\u306e\u69cb\u9020\u3092\u5229\u7528\u3057\u305f\u30de\u30c8\u30ea\u30c3\u30af\u30b9\u5206\u6790\u624b\u6cd5\u306e\u63d0\u6848\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621025\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9f4b\u85e4\u6d0b\uff0c\u5c71\u5d0e\u53f2\u535a\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e9\u30f3\u30c0\u30e0\u306a\u6b21\u5143\u524a\u6e1b\u3068\u30a2\u30f3\u30b5\u30f3\u30d6\u30eb\u306b\u3088\u308b\u30e1\u30c8\u30ea\u30c3\u30af\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u8a08\u7b97\u91cf\u4f4e\u6e1b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621025\u5e74\u5ea6\u79cb\u5b63\u7814\u7a76\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u9ad8\u6b21\u5143\u304b\u3064\u30b9\u30d1\u30fc\u30b9\u306a\u30d9\u30af\u30c8\u30eb\u7a7a\u9593\u306b\u304a\u3051\u308b\u21131\u6b63\u5247\u5316\u306b\u57fa\u3065\u304f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u68ee\u60a0\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5c0f\u5ddd\u664b\u4e00\u90ce\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c31\u8077\u30dd\u30fc\u30bf\u30eb\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u30ec\u30b3\u30e1\u30f3\u30c7\u30fc\u30b7\u30e7\u30f3\u30e2\u30c7\u30eb\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5742\u672c\u4fca\u8f14\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u9ad8\u6b21\u5143\uff0c\u30b9\u30d1\u30fc\u30b9\u306a\u30c7\u30fc\u30bf\u3092\u5bfe\u8c61\u3068\u3057\u305f\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u65e9\u5ddd\u771f\u592e\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5c0f\u5ddd\u664b\u4e00\u90ce\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5c64\u5225\u6728\u3068\u6df7\u5408\u30ef\u30a4\u30d6\u30eb\u5206\u5e03\u306b\u57fa\u3065\u304f\u5c31\u8077\u6d3b\u52d5\u7d42\u4e86\u6642\u671f\u306e\u4e88\u6e2c\u30e2\u30c7\u30eb\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u4e95\u8cb4\u88d5\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8a55\u4fa1\u3068\u8cfc\u8cb7\u306e\u4e21\u5c65\u6b74\u30c7\u30fc\u30bf\u3092\u7528\u3044\u308b\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u306e\u63a8\u5b9a\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u837b\u539f\u5927\u9678\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cReed Muller\u7b26\u53f7\u3092\u7528\u3044\u305f\u968e\u5c64\u7684ECOC\u6cd5\u306b\u3088\u308b\u591a\u5024\u6587\u66f8\u5206\u985e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2013\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5cef\u82eb\u548c\u53f2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5c0f\u5ddd\u664b\u4e00\u90ce\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u3092\u7528\u3044\u305f\u5b66\u751f\u306e\u5c31\u8077\u6d3b\u52d5\u30a8\u30f3\u30c8\u30ea\u30fc\u4e88\u6e2c\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c36\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\uff08SITA2013\uff09\uff0c2013\u5e7411\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e95\u4e0a\u5927\u6a39\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6587\u66f8\u5206\u985e\u554f\u984c\u306b\u304a\u3051\u308b\u30ab\u30c6\u30b4\u30ea\u306b\u6ce8\u76ee\u3057\u305f\u53ef\u5909\u9577N\u30b0\u30e9\u30e0\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u4e95\u8cb4\u88d5\uff0c\u8352\u5ddd\u8cb4\u7d00\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u624b\u6cd5\u3092\u5c0e\u5165\u3057\u305fImproved Naive Bayes\u6cd5\u306b\u3088\u308b\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5742\u672c\u4fca\u8f14\uff0c\u4e95\u6ca2\u7950\u4ecb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6df7\u5408\u5236\u7d04\u4ed8\u304d\u6f5c\u5728\u30c7\u30a3\u30ea\u30af\u30ec\u914d\u5206\u6cd5\u306b\u57fa\u3065\u304f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6587\u66f8\u5206\u985e\u554f\u984c\u306b\u304a\u3051\u308b\u30ab\u30c6\u30b4\u30ea\u60c5\u5831\u3092\u7528\u3044\u305f\u9069\u5fdc\u7684\u91cd\u307f\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5e73\u6fa4\u8302\u4e00\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30c6\u30ad\u30b9\u30c8\u5206\u985e\u554f\u984c\u306b\u304a\u3051\u308b\u30ab\u30c6\u30b4\u30ea\u60c5\u5831\u3092\u7528\u3044\u305f\u9069\u5fdc\u7684\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c11\u56de\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u60c5\u5831\u8ad6\u7684\u5b66\u7fd2\u7406\u8ad6\u3068\u6a5f\u68b0\u5b66\u7fd2\u7814\u7a76\u4f1a\uff08IBISML\uff09\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5927\u68ee\u60a0\u77e2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30e6\u30fc\u30b6\u306e\u8a55\u4fa1\u50be\u5411\u3068\u6f5c\u5728\u30af\u30e9\u30b9\u3092\u8003\u616e\u3057\u305f\u63a8\u85a6\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u837b\u539f\u5927\u9678\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7b26\u53f7\u7406\u8ad6\u306b\u57fa\u3065\u304fECOC\u6cd5\u306b\u3088\u308b\u591a\u5024\u30d1\u30bf\u30fc\u30f3\u5206\u985e\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u65e9\u5ddd\u771f\u592e\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5927\u548c\u7530\u52c7\u4eba\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30d5\u30a9\u30fc\u30af\u30bd\u30ce\u30df\u30fc\u3092\u5229\u7528\u3057\u305f\u5358\u8a9e\u9593\u306e\u95a2\u9023\u306b\u3088\u308bWeb\u30da\u30fc\u30b8\u691c\u7d22\u30b7\u30b9\u30c6\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621024\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e95\u6ca2\u7950\u4ecb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30a8\u30fc\u30b8\u30a7\u30f3\u30c8\u30d9\u30fc\u30b9\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u306b\u3088\u308b\u30e2\u30c7\u30eb\u30d9\u30fc\u30b9\u5354\u8abf\u30d5\u30a3\u30eb\u30bf\u30ea\u30f3\u30b0\u306e\u8a55\u4fa1\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c35\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9234\u6728\u5065\u53f2\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u78ba\u7387\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304fAggregate Diversity\u3092\u8003\u616e\u3057\u305f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c35\u56de\u60c5\u5831\u7406\u8ad6\u3068\u305d\u306e\u5fdc\u7528\u30b7\u30f3\u30dd\u30b8\u30a6\u30e0\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u77f3\u6a4b\u60f3\u592a\u90ce\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cECOC\u6cd5\u306b\u304a\u3051\u308b\u5206\u985e\u5668\u306e\u4e88\u6e2c\u7cbe\u5ea6\u3092\u8003\u616e\u3057\u305f\u5206\u985e\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u65e5\u6bd4\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7279\u5fb4\u8a9e\u306b\u6ce8\u76ee\u3057\u305fSmith-Waterman\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306b\u57fa\u3065\u304f\u527d\u7a83\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306e\u81ea\u52d5\u691c\u51fa\u624b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u4eba\u5de5\u77e5\u80fd\u3068\u77e5\u8b58\u51e6\u7406\u7814\u7a76\u4f1a\uff08AI\uff09\u4e88\u7a3f\u96c6\uff0c2012\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5c0f\u7530\u4e95\u826f\u8f14\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4e8c\u5024\u5224\u5225\u5668\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u3088\u308bRVM\u591a\u5024\u6587\u66f8\u5206\u985e\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c10\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\uff08FIT2011\uff09\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u69ae\u679d\u96bc\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5546\u54c1\u306e\u6bd4\u8f03\u5c65\u6b74\u3068\u30e6\u30fc\u30b6\u30fc\u30ec\u30d3\u30e5\u30fc\u306b\u57fa\u3065\u304f\u63a8\u85a6\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7b2c10\u56de\u60c5\u5831\u79d1\u5b66\u6280\u8853\u30d5\u30a9\u30fc\u30e9\u30e0\uff08FIT2011\uff09\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u62e1\u5f35\u4f59\u5f26\u5c3a\u5ea6\u3092\u7528\u3044\u305f\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621023\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e95\u6ca2\u7950\u4ecb\uff0c\u69ae\u679d\u96bc\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30a2\u30a4\u30c6\u30e0\u8a55\u4fa1\u5024\u306e\u9ad8\u4f4e\u3092\u8003\u616e\u3057\u305f\u6df7\u5408\u30e1\u30f3\u30d0\u30fc\u30b7\u30c3\u30d7\u30d6\u30ed\u30c3\u30af\u30e2\u30c7\u30eb\u306b\u3088\u308b\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621023\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u8352\u5ddd\u8cb4\u7d00\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8907\u6570\u8981\u7d20\u304b\u3089\u69cb\u6210\u3055\u308c\u308b\u6587\u66f8\u30c7\u30fc\u30bf\u306e\u5206\u985e\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf \uff5e\u968e\u5c64\u6f5c\u5728\u30af\u30e9\u30b9\u30e2\u30c7\u30eb\u3068\u968e\u5c64EM\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\uff5e\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621023\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9234\u6728\u5065\u53f2\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u78ba\u7387\u6f5c\u5728\u7a7a\u9593\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u63a8\u85a6\u30b7\u30b9\u30c6\u30e0\u306b\u95a2\u3059\u308b\u7814\u7a76\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621023\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u65e5\u6bd4\u5065\u592a\uff0c\u96f2\u5c45\u7384\u9053\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30b3\u30fc\u30c7\u30a3\u30f3\u30b0\u30b9\u30bf\u30a4\u30eb\u30e2\u30c7\u30eb\u306b\u57fa\u3065\u304f\u527d\u7a83\u30bd\u30fc\u30b9\u30b3\u30fc\u30c9\u306e\u81ea\u52d5\u691c\u51fa\u624b\u6cd5\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621023\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2011\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u77f3\u7530\u5d07\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u6e80\u8db3\u5ea6\u3092\u8003\u616e\u3057\u305f\u30e6\u30fc\u30b6\u30ec\u30d3\u30e5\u30fc\u306e\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621022\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2010\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u69ae\u679d\u96bc\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5bbf\u6cca\u65bd\u8a2d\u3092\u5bfe\u8c61\u3068\u3057\u305f\u8a55\u4fa1\u30b5\u30a4\u30c8\u306b\u304a\u3051\u308b\u30e6\u30fc\u30b6\u30ec\u30d3\u30e5\u30fc\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621022\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2010\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u5bcc\u7530\u5927\u4ecb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5897\u4e95\u5fe0\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201cWeb\u4e0a\u306e\u30e6\u30fc\u30b6\u30b3\u30e1\u30f3\u30c8\u3092\u7528\u3044\u305f\u4fa1\u683c\u30d7\u30ec\u30df\u30a2\u30e0\uff0d\u9867\u5ba2\u4fa1\u5024\u69cb\u9020\u30e2\u30c7\u30eb\u306e\u69cb\u7bc9\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a2008\u5e74\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2008\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e21\u8fba\u667a\u5e78\uff0c\u6d77\u8001\u6fa4\u5353\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u77e5\u8b58\u69cb\u9020\u3092\u5229\u7528\u3057\u305f\u6587\u66f8\u30c7\u30fc\u30bf\u306e\u81ea\u52d5\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621019\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2007\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6e21\u8fba\u667a\u5e78\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u81ea\u7531\u8a18\u8ff0\u6587\u66f8\u30c7\u30fc\u30bf\u5206\u6790\u306b\u304a\u3051\u308b\u77e5\u8b58\u69cb\u9020\u306e\u63f4\u7528\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621019\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2007\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6817\u5cf6\u7531\u7f8e\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u30d6\u30ec\u30f3\u30c0\u30fb\u30d6\u30c3\u30b7\u30a7\u30eb\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30cd\u30d1\u30fc\u30eb-\u65e5\u672c\u306e\u9023\u643a\u306b\u3088\u308b\u5c0f\u5b66\u751f\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30d7\u30ed\u30b0\u30e9\u30e0\u306e\u8a2d\u8a08\u3068\u8a55\u4fa1\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u74b0\u5883\u6559\u80b2\u5b66\u4f1a\u7b2c17\u56de\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2006\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u6817\u5cf6\u7531\u7f8e\uff0c\u30d6\u30ec\u30f3\u30c0\u30fb\u30d6\u30c3\u30b7\u30a7\u30eb\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5927\u5b66\u751f\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30cd\u30d1\u30fc\u30eb\u30d7\u30ed\u30b8\u30a7\u30af\u30c8\u306e\u6559\u80b2\u52b9\u679c\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u74b0\u5883\u6559\u80b2\u5b66\u4f1a\u7b2c17\u56de\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2006\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u6d5c\u7fd4\u592a\u90ce\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u5927\u5b66\u30de\u30fc\u30b1\u30c6\u30a3\u30f3\u30b0\u306e\u305f\u3081\u306e\u5e02\u5834\u5206\u6790\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621018\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2006\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u9ad8\u6a4b\u52c9\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u7d99\u7d9a\u8cfc\u8cb7\u306b\u3064\u306a\u304c\u308b\u9867\u5ba2\u30ed\u30a4\u30e4\u30eb\u30c6\u30a3\u306e\u69cb\u9020\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621018\u5e74\u5ea6\u6625\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2006\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u9867\u5ba2\u30ed\u30a4\u30e4\u30eb\u30c6\u30a3\u69cb\u9020\u56f3\u306b\u57fa\u3065\u304f\u30e6\u30fc\u30b6\u30b3\u30e1\u30f3\u30c8\u306e\u5206\u6790\u624b\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621018\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2006\u5e74\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff0c\u30d6\u30ec\u30f3\u30c0\u30fb\u30d6\u30c3\u30b7\u30a7\u30eb\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u30cd\u30d1\u30fc\u30eb\u306b\u304a\u3051\u308b\u74b0\u5883NGO\u306e\u30b3\u30df\u30e5\u30cb\u30c6\u30a3\u30de\u30cd\u30b8\u30e1\u30f3\u30c8\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u5e73\u621017\u5e74\u5ea6\u79cb\u5b63\u5927\u4f1a\u4e88\u7a3f\u96c6\uff0c2005\u5e74\uff0e<\/span>\r\n          <\/li>\r\n        <\/ol>\r\n      <\/details>\r\n\r\n      <details class=\"ml-pubdetails\" id=\"other\">\r\n        <summary>\u89e3\u8aac\u30fb\u7d00\u8981\u7b49<span>3\u4ef6<\/span><\/summary>\r\n        <ol>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u21131\u6b63\u5247\u5316\u306b\u57fa\u3065\u304f\u8907\u6570\u306e\u8a08\u91cf\u884c\u5217\u3092\u7528\u3044\u305f\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u6e58\u5357\u5de5\u79d1\u5927\u5b66\u7d00\u8981\uff0cVol. 52\uff0cNo. 1\uff0cpp. 53\u201360\uff0c2018\u5e742\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u4e09\u5ddd\u5065\u592a\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u8a08\u91cf\u8ddd\u96e2\u5b66\u7fd2\u624b\u6cd5\u306b\u3088\u308b\u30d1\u30bf\u30fc\u30f3\u8a8d\u8b58\u3068\u305d\u306e\u77e5\u8b58\u767a\u898b\u3078\u306e\u5fdc\u7528\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u30b7\u30b9\u30c6\u30e0\uff0cVol. 25\uff0cNo. 4\uff0c2016\u5e741\u6708\uff0e<\/span>\r\n          <\/li>\r\n          <li>\r\n            <span class=\"ml-pub-au\">\u9152\u4e95\u62d3\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff1a<\/span>\r\n            <span class=\"ml-pub-ti\">\u201c\u4f1a\u54e1\u30b9\u30c6\u30fc\u30b8\u306b\u7740\u76ee\u3057\u305f\u512a\u826f\u9867\u5ba2\u30fb\u96e2\u53cd\u9867\u5ba2\u306e\u7279\u5fb4\u5206\u6790\u201d\uff0c<\/span>\r\n            <span class=\"ml-pub-src\">\u7d4c\u55b6\u30b7\u30b9\u30c6\u30e0\uff0cVol. 25\uff0cNo. 3\uff0cpp. 182\u2013187\uff0c2015\u5e7410\u6708\uff0e<\/span>\r\n    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