{"id":59,"date":"2022-12-22T09:43:25","date_gmt":"2022-12-22T00:43:25","guid":{"rendered":"https:\/\/www.comm.tcu.ac.jp\/mikawalab\/?page_id=59"},"modified":"2026-04-15T11:57:57","modified_gmt":"2026-04-15T02:57:57","slug":"%e5%ad%a6%e8%a1%93%e8%ab%96%e6%96%87","status":"publish","type":"page","link":"https:\/\/www.comm.tcu.ac.jp\/mikawalab\/%e7%a0%94%e7%a9%b6%e6%a5%ad%e7%b8%be\/%e5%ad%a6%e8%a1%93%e8%ab%96%e6%96%87","title":{"rendered":"\u5b66\u8853\u8ad6\u6587"},"content":{"rendered":"\r\n

\u5b66\u8853\u8ad6\u6587<\/h4>\r\n\r\n\r\n\r\n
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  1. Y. Sakai, K. Mikawa, M. Goto, \"High-dimensional Labeled Data Embedding and Visualization Method Using von Mises Distribution, \" Total Quality Science, Vol.11, No.1, pp.22-29, 2025\u5e7411\u6708\u3002<\/li>\r\n
  2. K. Mikawa,\u00a0M. Kobayashi,\u00a0T. Sasaki,\u00a0and A. Manada,\u00a0\"A study on latent structural models for binary relational data with attribute information,\u00a0\" Nonlinear Theory and Its Applications, IEICE,\u00a0\u00a0Volume 15,\u00a0Issue 2,\u00a0Pages 335-353,\u00a02024\u5e744\u6708\uff0e<\/li>\r\n\r\n\r\n\r\n
  3. \u9752\u6728\u7ae0\u609f, \u4e09\u5ddd\u5065\u592a, \u5f8c\u85e4\u6b63\u5e78: \"Web\u95b2\u89a7\u5c65\u6b74\u306b\u57fa\u3065\u304f\u30bf\u30fc\u30b2\u30c3\u30c6\u30a3\u30f3\u30b0\u306e\u305f\u3081\u306e\u5c5e\u6027\u30e9\u30d9\u30eb\u5b66\u7fd2\u6cd5\", \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c\uff0cVol. 73, No. 1\uff0cpp.1 - 14, 2022\u5e744\u6708\uff0e<\/li>\r\n
  4. R. Uehara, T. Matsumoto, K. Mikawa, and M. Goto, \"A Prediction Model of Runs Allowed Based on Latent-Class Markov Chain for Starters of Professional Baseball Pitchers, \" Total Quality Science, Vol.7, No.2, pp.69-81, 2022\u5e742\u6708\uff0e<\/li>\r\n
  5. \u6851\u7530 \u548c\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u4f50\u3005\u6728 \u5317\u90fd\uff0c\u5f8c\u85e4\u6b63\u5e78: \"\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\", \u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol.63, No.1, pp.218-230, 2022\u5e741\u6708,<\/li>\r\n\r\n\r\n\r\n
  6. Y. Sakai, K. Yasui, K. Mikawa, and M. Goto: An Extension of Semi-supervised Boosting to Multi-valued Classification Problems, Total Quality Science, Vol. 6, No. 2, pp. 60 - 69, 2021\u5e742\u6708.<\/li>\r\n\r\n\r\n\r\n
  7. \u6851\u7530 \u548c\uff0c\u6749\u5d0e\u667a\u54c9\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 62, No. 2, pp. 796 - 808, 2021\u5e741\u6708.\u00a0<\/li>\r\n\r\n\r\n\r\n
  8. T. Sugisaki, K. Mikawa, M. Goto: Factorization Machines Considering the Latent Characteristics behind Target Data, Asian Journal of Management Science and Applications, Vol.5, No.2, pp. 111 - 128, 2021\u5e741\u6708.<\/li>\r\n\r\n\r\n\r\n
  9. Q. Zhang, H. Yamashita, K. Mikawa, M. Goto: Analysis of Purchase History Data Based on a New Latent Class Model for RFM Analysis, Industrial Engineering & Management Systems, Vol. 19, No. 2, pp. 476 - 483, 2020\u5e746\u6708.<\/li>\r\n\r\n\r\n\r\n
  10. S. Okayama, H. Yamashita, K. Mikawa, M. Goto, T. Yoshikai : Relational analysis model of weather conditions and sales patterns based on nonnegative tensor factorization, International Journal of Production Research, Vol. 58, No. 8, pp. 2477 - 2489, 2019\u5e7412\u6708.<\/li>\r\n\r\n\r\n\r\n
  11. \u5b89\u4e95\u4e00\u8cb4\uff0c\u4e2d\u91ce\u4fee\u5e73\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c, Vol. 28, No. 2, pp. 69 - 87, 2019\u5e749\u6708.<\/li>\r\n\r\n\r\n\r\n
  12. S. Nagamori, K. Mikawa, M. Goto, T. Ogihara: An Analytic Model to Represent Relation between Finish Date of Job-Hunting and Time-Series Variation of Entry Tendencies, Industrial Engineering & Management Systems, Vol. 18, No. 3, pp. 292 - 304, 2019\u5e749\u6708.<\/li>\r\n\r\n\r\n\r\n
  13. \u4ec1\u30ce\u5e73 \u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 60, No. 4, pp. 1151 - 1161, 2019\u5e744\u6708.<\/li>\r\n\r\n\r\n\r\n
  14. \u99ac\u8cc0\u5d69\u58eb, \u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78\uff0c\u5409\u958b\u670b\u5f18: \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\uff0c\u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8cD, Vol. J101-D, No. 7, pp. 1037 - 1051, 2018\u5e747\u6708,.<\/li>\r\n\r\n\r\n\r\n
  15. \u95a2\u53e3\u3042\u3086\u307f, \u4ec1\u30ce\u5e73 \u5c06\u4eba\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c, Vol. 27, No. 1, pp. 67 - 78, 2018\u5e746\u6708.<\/li>\r\n\r\n\r\n\r\n
  16. \u677e\u5d5c\u7950\u6a39, \u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 58, No. 12, pp. 2034 - 2045, 2017\u5e7412\u6708.<\/li>\r\n\r\n\r\n\r\n
  17. S. Misawa, K. Mikawa, and M. Goto: Adaptive Prediction Method Based on Alternating Decision Forests with Considerations for Generalization Ability, Industrial Engineering & Management Systems, Vol. 16, No. 3, pp. 384 - 391, 2017\u5e7410\u6708.<\/li>\r\n\r\n\r\n\r\n
  18. L. Suzuki, K. Mikawa, and M. Goto\uff1aMulti-Valused Classification of Text Data Based on an ECOC Approach Using a Ternary Orthogonal Table\uff0cIndustrial Engineering & Management Systems, Vol. 16, No. 2, pp. 155 - 164, 2017\u5e747\u6708.<\/li>\r\n\r\n\r\n\r\n
  19. Y. Yamamoto, K. Mikawa, and M. Goto: A Proposal for Classification of Document Data with Unobserved Categories Considering Latent Topics\uff0cIndustrial Engineering & Management Systems, Vol. 16, No. 2, pp. 165 - 174, 2017\u5e747\u6708.<\/li>\r\n\r\n\r\n\r\n
  20. \u6e6f\u5ddd \u8f1d\u4e00\u6717, \u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 68, No. 2, pp. 86 - 98, 2017\u5e747\u6708.<\/li>\r\n\r\n\r\n\r\n
  21. \u85e4\u539f\u76f4\u5e83, \u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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\uff0c\u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8a8c, Vol. 26, No. 1, pp. 1 - 16, 2017\u5e746\u6708.<\/li>\r\n\r\n\r\n\r\n
  22. \u65e9\u5ddd\u771f\u592e, \u4e09\u5ddd\u5065\u592a\uff0c\u837b\u539f\u5927\u9678\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u60c5\u5831\u51e6\u7406\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 58, No. 5, pp. 1189 - 1206, 2017\u5e745\u6708.<\/li>\r\n\r\n\r\n\r\n
  23. T. Maga, K. Mikawa, and M. Goto: Data pair selection for accurate classification based on information-theoretic metric learning, Asian Journal of Management Science and Applications, Vol. 3,\u00a0 No. 1, pp. 61 - 74, 2017\u5e744\u6708.<\/li>\r\n\r\n\r\n\r\n
  24. \u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 66, No. 4, pp. 335 - 347, 2016\u5e741\u6708.<\/li>\r\n\r\n\r\n\r\n
  25. \u4e09\u5ddd\u5065\u592a\uff0c\u5c0f\u6797\u5b66\uff0c\u5f8c\u85e4\u6b63\u5e78: \u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u306b\u57fa\u3065\u304fl_1\u00a0 \u6b63\u5247\u5316\u3092\u7528\u3044\u305f\u8a08\u91cf\u884c\u5217\u306e\u5b66\u7fd2\u6cd5\u306b\u95a2\u3059\u308b\u4e00\u8003\u5bdf, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 66, No. 3, pp. 230 - 239, 2015\u5e7411\u6708.<\/li>\r\n\r\n\r\n\r\n
  26. K. Mikawa, and M.Goto: Regularized Distance Metric Learning for the Document Classification and its Application, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 66, No. 2E, pp. 190 - 203, 2015\u5e747\u6708.<\/li>\r\n\r\n\r\n\r\n
  27. \u5927\u4e95\u8cb4\u88d5\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 65, No. 4, pp. 286 - 293, 2015\u5e741\u6708.<\/li>\r\n\r\n\r\n\r\n
  28. \u4e0b\u6751\u826f\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 65, No. 2, pp. 51 - 60, 2014\u5e747\u6708.<\/li>\r\n\r\n\r\n\r\n
  29. T. Suzuki, G. Kumoi, K. Mikawa, and M. Goto: A Design of Recommendation Based on Flexible Mixture Model Considering Purchasing Interest and Post-Purchase Satisfaction, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 64, No. 4E, pp. 570 - 578, 2014\u5e741\u6708.<\/li>\r\n\r\n\r\n\r\n
  30. \u4e95\u6ca2\u7950\u4ecb\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u7d4c\u55b6\u60c5\u5831\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 22, No. 2, pp. 1 - 22, 2013\u5e749\u6708.<\/li>\r\n\r\n\r\n\r\n
  31. T. Ogihara, K. Mikawa, G. Hosoya, and M. Goto,: Multi-valued Document Classification based on coding theory, China-USA Business Review, Vol. 12, No. 9, pp. 911 - 917, 2013\u5e749\u6708.<\/li>\r\n\r\n\r\n\r\n
  32. \u8352\u5ddd\u8cb4\u7d00\uff0c\u4e09\u5ddd\u5065\u592a\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u96fb\u5b50\u60c5\u5831\u901a\u4fe1\u5b66\u4f1a\u8ad6\u6587\u8a8cD, Vol. J96-D, No. 8, pp. 1955 - 1959, 2013\u5e748\u6708.<\/li>\r\n\r\n\r\n\r\n
  33. K. Mikawa, T. Ishida and M. Goto: An Optimal Weighting Method in Supervised Learning of Linguistic Model for Text Classification, Industrial Engineering & Management Systems, Vol.11 No. 1, pp.87-93, 2012\u5e741\u6708.<\/li>\r\n\r\n\r\n\r\n
  34. \u4e09\u5ddd\u5065\u592a\uff0c\u5897\u4e95\u5fe0\u5e78\uff0c\u5f8c\u85e4\u6b63\u5e78: \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, \u65e5\u672c\u7d4c\u55b6\u5de5\u5b66\u4f1a\u8ad6\u6587\u8a8c, Vol. 59, No. 5, pp. 365 - 375, 2008\u5e7412\u6708.<\/li>\r\n\r\n\r\n\r\n
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