{"id":4706,"date":"2026-10-29T15:39:46","date_gmt":"2026-10-29T06:39:46","guid":{"rendered":"http:\/\/int-info.com\/?page_id=4706"},"modified":"2026-10-29T15:41:12","modified_gmt":"2026-10-29T06:41:12","slug":"resv2","status":"publish","type":"page","link":"http:\/\/int-info.com\/index.php\/resv2\/","title":{"rendered":"\u7814\u7a76\u7d39\u4ecb"},"content":{"rendered":"<p>\u4eca\u3084\u3001\u6a5f\u68b0\u5b66\u7fd2\u306f\u73fe\u5728\u306e\u4e16\u754c\u3092\u652f\u3048\u308b\u57fa\u76e4\u6280\u8853\u306e\u4e00\u3064\u3067\u3059\u3002\u751f\u6210AI\u306e\u767b\u5834\u306b\u3088\u308a\u3001\u5c11\u3057\u52c9\u5f37\u3059\u308b\u3060\u3051\u3067\u3001\u7c21\u5358\u306b\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u672c\u7814\u7a76\u5ba4\u3067\u3001\u3053\u306e\u4e16\u306e\u4e2d\u306e\u6d41\u308c\u3092\u52a0\u901f\u3055\u305b\u308b\u305f\u3081\u306e\u7814\u7a76\u306b\u53d6\u308a\u7d44\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n<hr>\n<p><strong>\u5b9f\u793e\u4f1a\u3067\u306e\u6a5f\u68b0\u5b66\u7fd2\u306e\u5fdc\u7528\uff1a<\/strong><br \/>\n\u4e16\u306e\u4e2d\u3092\u3088\u308a\u826f\u304f\u3059\u308b\u304a\u624b\u4f1d\u3044\u3068\u3057\u3066\u3001\u5f79\u7acb\u3064\u6a5f\u68b0\u5b66\u7fd2\u306e\u5b9f\u88c5\u3082\u884c\u306a\u3063\u3066\u3044\u307e\u3059\u3002\u533b\u7642\u5fdc\u7528\uff08\u65e5\u5927\u533b\u5b66\u90e8\u3001\u9806\u5929\u5802\u5927\u5b66\u3001\u7fa4\u99ac\u5927\u5b66\u3068\u306e\u5171\u540c\u7814\u7a76\uff09\u3001\u91cd\u529b\u6ce2\u691c\u51fa\uff08\u6771\u4eac\u90fd\u5e02\u5927\u5b66\u5b87\u5b99\u79d1\u5b66\u7814\u7a76\u30bb\u30f3\u30bf\u30fc\u3068\u306e\u5171\u540c\u7814\u7a76\uff09\u3001\u30b9\u30dd\u30fc\u30c4\u5fdc\u7528\uff08\u56fd\u7acb\u30b9\u30dd\u30fc\u30c4\u79d1\u5b66\u30bb\u30f3\u30bf\u30fc\u3068\u306e\u5171\u540c\u7814\u7a76\uff09\u3001COVID-19\u306e\u611f\u67d3\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\uff08\u5185\u95a3\u5e9c\u30b3\u30ed\u30ca\u5bfe\u7b56\u5ba4\u306e\u4f9d\u983c\u306b\u3088\u308b\uff09\u3001\u5b66\u3073\u5408\u3044\u3092\u6700\u9069\u5316\u3059\u308b\u77e5\u80fd\u30b7\u30b9\u30c6\u30e0\uff08\u5c71\u68a8\u770c\u7acb\u5927\u5b66\u3001\u4e0a\u8d8a\u6559\u80b2\u5927\u5b66\u3068\u306e\u5171\u540c\u7814\u7a76\uff09\u306a\u3069\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<ul>\n<li> <a href=\"https:\/\/doi.org\/10.1002\/ehf2.14282\" rel=\"noopener\" target=\"_blank\"> Y. Saito, Y. Omae, Prognostic significance of pulmonary arterial wedge pressure estimated by deep learning in acute heart failure, ESC Heart Failure, vol.10, no.2, pp.1103-1113, 2022. <\/a>\n<\/li>\n<li>\n<a href=\"https:\/\/journals.aps.org\/prd\/abstract\/10.1103\/PhysRevD.109.043011\" rel=\"noopener\" target=\"_blank\">S. Sasaoka, Y. Omae et al., Comparative study of 1D and 2D convolutional neural network models with attribution analysis for gravitational wave detection from compact binary coalescences, Physical Review D, vol.109, no.4, art. no. 043011, 2024.<\/a>\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u96fb\u529b\u5909\u63db\u5668\u306e\u30c7\u30c3\u30c9\u30bf\u30a4\u30e0\u88dc\u511f\u306e\u81ea\u52d5\u8abf\u6574: <\/strong><br \/>\n\u96fb\u529b\u5909\u63db\u5668\u3067\u306f\u3001\u30c7\u30c3\u30c9\u30bf\u30a4\u30e0\u306b\u3088\u3063\u3066\u51fa\u529b\u96fb\u5727\u306b\u3072\u305a\u307f\u304c\u751f\u3058\u307e\u3059\u3002\u305d\u306e\u88dc\u511f\u6307\u4ee4\u5024\u306f\u8a2d\u8a08\u6642\u306b\u8a2d\u5b9a\u3055\u308c\u307e\u3059\u304c\u3001\u7d20\u5b50\u306e\u4ea4\u63db\u3084\u52d5\u4f5c\u6761\u4ef6\u306e\u5909\u5316\u306b\u3088\u3063\u3066\u6700\u9069\u5024\u304b\u3089\u305a\u308c\u3066\u3057\u307e\u3044\u307e\u3059\u3002\u305d\u3053\u3067\u3001\u6307\u4ee4\u5024\u3092\u81ea\u52d5\u8abf\u6574\u3059\u308b\u624b\u6cd5\u3068\u3057\u3066\u3001\u6ce2\u5f62\u7279\u5fb4\u91cf\u306b\u3088\u308b\u56de\u5e30\u30e2\u30c7\u30eb\u3001\u56de\u5e30\u578b\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb\u30cd\u30c3\u30c8\u30ef\u30fc\u30af(CNN)\u3092\u6bd4\u8f03\u3057\u3001CNN\u304c\u512a\u308c\u308b\u3053\u3068\u3092\u793a\u3057\u307e\u3057\u305f\u3002\u5916\u90e8\u6f14\u7b97\u56de\u8def\u3092\u7528\u3044\u305f\u88dc\u511f\u91cf\u306e\u81ea\u52d5\u8abf\u6574\u6cd5\u3082\u691c\u8a0e\u3057\u3066\u3044\u307e\u3059\u3002\u672c\u7814\u7a76\u306fIEEJ\u304b\u3089\u51fa\u7248\u3055\u308c\u307e\u3057\u305f\u3002<\/p>\n<ul>\n<li> <a href=\"https:\/\/www.jstage.jst.go.jp\/article\/ieejjia\/14\/6\/14_24014418\/_article\/-char\/ja\" rel=\"noopener\" target=\"_blank\">Machine Learning-Based Methods for Automatic Compensation of Dead-time Distortion: A Comparative Study<\/a>\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u6df1\u5c64\u5b66\u7fd2\u306e\u6c4e\u5316\u7406\u8ad6\uff08\u640d\u5931\u3078\u30b7\u30a2\u30f3\u306e\u89b3\u70b9\u304b\u3089\uff09\uff1a<\/strong><br \/>\n\u6df1\u5c64\u5b66\u7fd2\u306f\u591a\u69d8\u306a\u56de\u5e30\u30fb\u5206\u985e\u8ab2\u984c\u306b\u304a\u3044\u3066\u512a\u308c\u305f\u4e88\u6e2c\u6027\u80fd\u3092\u793a\u3059\u3053\u3068\u304b\u3089\u3001\u793e\u4f1a\u306e\u5404\u5206\u91ce\u3067\u793e\u4f1a\u5b9f\u88c5\u304c\u9032\u3093\u3067\u3044\u307e\u3059\u3002\u73fe\u5728\u3001\u5fdc\u7528\u7814\u7a76\u306b\u304a\u3051\u308b\u81a8\u5927\u306a\u8a66\u884c\u932f\u8aa4\u306e\u7d50\u679c\u3001\u6027\u80fd\u5411\u4e0a\u306b\u5bc4\u4e0e\u3059\u308b\u624b\u6cd5\u304c\u7d4c\u9a13\u7684\u306b\u84c4\u7a4d\u3055\u308c\u3064\u3064\u3042\u308a\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u305d\u306e\u77e5\u898b\u306e\u591a\u304f\u306f\u7d4c\u9a13\u7684\u30fb\u7d71\u8a08\u7684\u306a\u8a00\u53ca\u306b\u3068\u3069\u307e\u3063\u3066\u304a\u308a\u3001\u7279\u5b9a\u306e\u74b0\u5883\u5916\u3067\u306e\u518d\u73fe\u6027\u3084\u4fe1\u983c\u6027\u304c\u62c5\u4fdd\u3055\u308c\u306a\u3044\u3068\u3044\u3046\u5927\u304d\u306a\u554f\u984c\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u672c\u7814\u7a76\u306f\u3001\u6df1\u5c64\u5b66\u7fd2\u304c\u3046\u307e\u304f\u3044\u304f\/\u5931\u6557\u3059\u308b\u6761\u4ef6\u3092\u6570\u5b66\u7684\u306b\u53b3\u5bc6\u306b\u8a18\u8ff0\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u3044\u307e\u3059\u3002\u7279\u306b\u3001\u5e73\u5766\u89e3\u3001PAC\u30d9\u30a4\u30ba\u6c4e\u5316\u30ae\u30e3\u30c3\u30d7\u3001\u3078\u30b7\u30a2\u30f3\u89e3\u6790\u306a\u3069\u306b\u6ce8\u76ee\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li> <a href=\"http:\/\/arxiv.org\/abs\/2606.28662\" rel=\"noopener\" target=\"_blank\">[Abst.]<\/a> <a href=\"https:\/\/arxiv.org\/pdf\/2606.28662\" rel=\"noopener\" target=\"_blank\">[pdf]<\/a> Y. Omae, Closed-Form Steepest Descent Direction toward Flat Minima: Reducing Upper Bounds on the Loss Hessian Eigenspectrum in Neural Networks<\/li>\n<li> <a href=\"https:\/\/arxiv.org\/abs\/2604.10202\" rel=\"noopener\" target=\"_blank\">[Abst.]<\/a> <a href=\"https:\/\/arxiv.org\/pdf\/2604.10202\" rel=\"noopener\" target=\"_blank\">[pdf]<\/a> Y. Omae, Wolkowicz-Styan Upper Bound on the Hessian Eigenspectrum for Cross-Entropy Loss in Nonlinear Smooth Neural Networks<\/li>\n<\/ul>\n<hr>\n<p><strong>\u30e0\u30c0\u306e\u306a\u3044\u6df1\u5c64\u5b66\u7fd2\u306e\u69cb\u7bc9\u624b\u6cd5\uff1a<\/strong><br \/>\n\u6df1\u5c64\u5b66\u7fd2\u3067\u306f\u3001\u4e88\u6e2c\u306b\u5bc4\u4e0e\u3057\u306a\u3044\u60c5\u5831\u3092\u5165\u529b\u3059\u308b\u3053\u3068\u3084\u3001\u4e0d\u8981\u306a\u7279\u5fb4\u3092\u7528\u3044\u308b\u3053\u3068\u304c\u3001\u8a08\u7b97\u30b3\u30b9\u30c8\u306e\u5897\u52a0\u3084\u4e88\u6e2c\u6027\u80fd\u30fb\u8aac\u660e\u53ef\u80fd\u6027\u306e\u4f4e\u4e0b\u306b\u3064\u306a\u304c\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u305d\u3053\u3067\u672c\u7814\u7a76\u3067\u306f\u3001\u6df1\u5c64\u5b66\u7fd2\u306b\u542b\u307e\u308c\u308b\u4e0d\u8981\u306a\u60c5\u5831\u3092\u7279\u5b9a\u30fb\u9664\u53bb\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u52b9\u7387\u7684\u306a\u6df1\u5c64\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u624b\u6cd5\u306b\u3064\u3044\u3066\u7814\u7a76\u3057\u3066\u3044\u307e\u3059\u3002\u7279\u306b\u3001\u5165\u529b\u4fe1\u53f7\u3084\u7279\u5fb4\u30de\u30c3\u30d7\u306b\u7740\u76ee\u3057\u3001\u4e88\u6e2c\u306b\u91cd\u8981\u306a\u60c5\u5831\u3092\u6b8b\u3057\u306a\u304c\u3089\u4e0d\u8981\u306a\u60c5\u5831\u3092\u524a\u6e1b\u3059\u308b\u624b\u6cd5\u3092\u958b\u767a\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<a href=\"https:\/\/www.sciencedirect.com\/science\/article\/abs\/pii\/S1568494624005830\" rel=\"noopener\" target=\"_blank\">[url]<\/a> Y. Omae et al., Deep learned features selection algorithm: Removal operation of anomaly feature maps (RO-AFM)\n<\/li>\n<li>\n<a href=\"https:\/\/www.aimspress.com\/article\/doi\/10.3934\/math.2024041\" rel=\"noopener\" target=\"_blank\">[url]<\/a> Y. Omae et al., Features Gradient-based Signals Selection Algorithm of Linear Complexity for Convolutional Neural Networks\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u4efb\u610f\u306e\u6df1\u5c64\u5b66\u7fd2\u306e\u63a8\u5b9a\u6839\u62e0\u306e\u53ef\u8996\u5316\u6cd5\uff1a<\/strong><br \/>\n\u6df1\u5c64\u5b66\u7fd2\u306e\u63a8\u5b9a\u6839\u62e0\u3092\u53ef\u8996\u5316\u3059\u308b\u624b\u6cd5\u3068\u3057\u3066Grad-CAM\u304c\u811a\u5149\u3092\u6d74\u3073\u3066\u3044\u307e\u3059\u304c\u3001\u672c\u624b\u6cd5\u306f\u6a19\u6e96\u7684\u306a\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u69cb\u9020\u306b\u3057\u304b\u9069\u7528\u3067\u304d\u306a\u3044\u3068\u3044\u3046\u5236\u7d04\u304c\u3042\u308a\u307e\u3059\u3002\u8fd1\u5e74\u3001\u30e2\u30c7\u30eb\u306e\u9ad8\u6027\u80fd\u5316\u3092\u76ee\u7684\u3068\u3057\u3066\u3001PCA\uff08\u4e3b\u6210\u5206\u5206\u6790\uff09\u3084SVM\uff08\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30f3\uff09\u3092\u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u5185\u90e8\u306b\u7d44\u307f\u8fbc\u3093\u3060\u30cf\u30a4\u30d6\u30ea\u30c3\u30c9\u30e2\u30c7\u30eb\u304c\u767b\u5834\u3057\u3066\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u3053\u308c\u3089\u306e\u7279\u6b8a\u306a\u30ec\u30a4\u30e4\u3092\u542b\u3080\u30e2\u30c7\u30eb\u306b\u5bfe\u3057\u3066\u306f\u3001Grad-CAM\u3092\u9069\u7528\u3067\u304d\u306a\u3044\u3068\u3044\u3046\u8ab2\u984c\u304c\u3042\u308a\u307e\u3057\u305f\u3002\u305d\u306e\u4e3b\u306a\u8981\u56e0\u306f\u3001Grad-CAM\u306e\u8a08\u7b97\u306b\u4e0d\u53ef\u6b20\u306a\u30e4\u30b3\u30d3\u884c\u5217\uff08\u5165\u51fa\u529b\u9593\u306e1\u968e\u5c0e\u95a2\u6570\uff09\u306e\u5c0e\u51fa\u304c\u56f0\u96e3\u3067\u3042\u308b\u305f\u3081\u3067\u3059\u3002\u305d\u3053\u3067\u672c\u7814\u7a76\u5ba4\u3067\u306f\u3001\u4efb\u610f\u306e\u30ec\u30a4\u30e4\u69cb\u9020\u3092\u6301\u3064\u30e2\u30c7\u30eb\u306b\u304a\u3044\u3066\u3082\u63a8\u5b9a\u6839\u62e0\u3092\u53ef\u8996\u5316\u3067\u304d\u308b\u3088\u3046\u3001\u5165\u51fa\u529b\u9593\u306e\u30e4\u30b3\u30d3\u884c\u5217\u306e\u5b9a\u5f0f\u5316\u3092\u884c\u3063\u3066\u3044\u307e\u3059\u3002\u3053\u308c\u307e\u3067\u306b\u3001\u5185\u633f\u3055\u308c\u305fPCA\u30ec\u30a4\u30e4\u3084SVM\u30ec\u30a4\u30e4\u306e\u53ef\u8996\u5316\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002\u3053\u306e\u3088\u3046\u306b\u3001\u30d6\u30e9\u30c3\u30af\u30dc\u30c3\u30af\u30b9\u3067\u89e3\u91c8\u304c\u56f0\u96e3\u306a\u6df1\u5c64\u5b66\u7fd2\u3092\u3001\u30db\u30ef\u30a4\u30c8\u30dc\u30c3\u30af\u30b9\u3067\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u6280\u8853\u3078\u3068\u6607\u83ef\u3055\u305b\u308b\u7814\u7a76\u3092\u63a8\u9032\u3057\u3066\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<a href=\"https:\/\/arxiv.org\/pdf\/2508.11880\" rel=\"noopener\" target=\"_blank\">Y. Omae, PCA- and SVM-Grad-CAM for Convolutional Neural Networks: Closed-form Jacobian Expression, [Machine Learning] (cs.LG), arXiv:2508.11880<\/a>\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u30b5\u30ed\u30b2\u30fc\u30c8\u6700\u9069\u5316\u306b\u304a\u3051\u308b\u4e0d\u78ba\u5b9a\u6027\u306e\u53b3\u5bc6\u306a\u5b9a\u91cf\u5316\uff1a<\/strong><br \/>\n\u5c11\u306a\u3044\u8a66\u884c\u56de\u6570\u3067\u6700\u9069\u89e3\u3092\u63a2\u7d22\u3059\u308b\u624b\u6cd5\u306f\u30b5\u30ed\u30b2\u30fc\u30c8\u6700\u9069\u5316\u3068\u547c\u3070\u308c\u3001\u5275\u85ac\u3084\u65b0\u6750\u6599\u958b\u767a\u3068\u3044\u3063\u305f\u9ad8\u30b3\u30b9\u30c8\u306a\u5b9f\u9a13\u3092\u4f34\u3046\u5206\u91ce\u3067\u6d3b\u7528\u3055\u308c\u3066\u3044\u307e\u3059\u3002\u672c\u624b\u6cd5\u306e\u30d7\u30ed\u30bb\u30b9\u306f\u3001\u4e0d\u78ba\u5b9f\u6027\u306e\u9ad8\u3044\u9818\u57df\u3092\u8abf\u67fb\u3059\u308b\u300c\u63a2\u7d22\u300d\u3068\u3001\u73fe\u6642\u70b9\u3067\u306e\u6709\u671b\u306a\u9818\u57df\u3092\u6df1\u6398\u308a\u3059\u308b\u300c\u6d3b\u7528\u300d\u306b\u5927\u5225\u3055\u308c\u307e\u3059\u3002\u4e00\u822c\u306b\u3001\u89b3\u6e2c\u5e8f\u76e4\u3067\u306f\u63a2\u7d22\u3092\u3001\u7d42\u76e4\u3067\u306f\u6d3b\u7528\u3092\u91cd\u8996\u3059\u3079\u304d\u3068\u3055\u308c\u307e\u3059\u304c\u3001\u7a7a\u9593\u5168\u4f53\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u5b9a\u91cf\u5316\u3059\u308b\u624b\u6cd5\u304c\u78ba\u7acb\u3055\u308c\u3066\u3044\u306a\u304b\u3063\u305f\u305f\u3081\u3001\u4e21\u8005\u306e\u8abf\u6574\u306f\u7d4c\u9a13\u7684\u306a\u30c8\u30ec\u30fc\u30c9\u30aa\u30d5\u306b\u983c\u3089\u3056\u308b\u3092\u5f97\u306a\u3044\u3068\u3044\u3046\u8ab2\u984c\u304c\u3042\u308a\u307e\u3057\u305f\u3002\u3053\u306e\u554f\u984c\u3092\u89e3\u6c7a\u3059\u308b\u305f\u3081\u3001\u672c\u7814\u7a76\u3067\u306f\u63a2\u7d22\u7a7a\u9593\u5168\u4f53\u306e\u4e0d\u78ba\u5b9f\u6027\u3092\u300c\u30ab\u30fc\u30cd\u30eb\u95a2\u6570\u3067\u8868\u73fe\u3055\u308c\u305f\u63a8\u5b9a\u5206\u6563\u306e\u7a4d\u5206\u5024\u300d\u3068\u5b9a\u7fa9\u3057\u3001\u305d\u306e\u7a4d\u5206\u306e\u9589\u5f62\u5f0f\u8868\u73fe\u3092\u5c0e\u51fa\u3059\u308b\u3053\u3068\u306b\u6210\u529f\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u7406\u8ad6\u7684\u6839\u62e0\u306b\u57fa\u3065\u3044\u305f\u52b9\u7387\u7684\u306a\u30b5\u30ed\u30b2\u30fc\u30c8\u6700\u9069\u5316\u304c\u53ef\u80fd\u3068\u306a\u308a\u307e\u3059\u3002\u73fe\u5728\u306f\u3001\u30ac\u30a6\u30b7\u30a2\u30f3\u30ab\u30fc\u30cd\u30eb\u3060\u3051\u3067\u306a\u304f\u3001\u3088\u308a\u8907\u96d1\u306a\u30ab\u30fc\u30cd\u30eb\u95a2\u6570\u306b\u304a\u3044\u3066\u3082\u4e0d\u78ba\u5b9f\u6027\u306e\u9589\u5f62\u5f0f\u8868\u73fe\u3092\u5f97\u308b\u3079\u304f\u3001\u305d\u306e\u62e1\u5f35\u306b\u53d6\u308a\u7d44\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<a href=\"https:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/10820123\/10872956.pdf\" rel=\"noopener\" target=\"_blank\">Y. Omae et al., EVI-GPBO: Estimated Variance Integration-Based Gaussian Process Bayesian Optimization, IEEE Access, vol.13,  pp.26208-26224, 2025.<\/a>\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u8a08\u7b97\u52b9\u7387\u306e\u826f\u3044\u30c7\u30fc\u30bf\u30de\u30a4\u30cb\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\uff1a<\/strong><br \/>\n\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306b\u306f\u3001\u5fdc\u7528\u4e0a\u610f\u5473\u306e\u3042\u308b\u30eb\u30fc\u30eb\uff08<a href=\"https:\/\/president.jp\/articles\/-\/15536\" rel=\"noopener\" target=\"_blank\">\u30aa\u30e0\u30c4\u3092\u8cb7\u3046\u4eba\u306f\u30d3\u30fc\u30eb\u3092\u8cb7\u3044\u3084\u3059\u3044<\/a>\u3001\u306a\u3069\uff09\u304c\u5927\u91cf\u306b\u6f5c\u3093\u3067\u3044\u307e\u3059\u3002\u3057\u304b\u3057\u3001\u65e2\u5b58\u306e\u30c7\u30fc\u30bf\u30de\u30a4\u30cb\u30f3\u30b0\u624b\u6cd5\u3084\u7d71\u8a08\u7684\u624b\u6cd5\u3067\u306f\u3001\u305d\u306e\u3046\u3061\u306e\u4e00\u90e8\u3057\u304b\u53d6\u308a\u51fa\u3059\u3053\u3068\u304c\u3067\u304d\u307e\u305b\u3093\u3002\u7d44\u307f\u5408\u308f\u305b\u7206\u767a\u306e\u554f\u984c\u3092\u62b1\u3048\u3066\u3044\u308b\u305f\u3081\u3001\u3042\u308b\u898f\u6a21\u306e\u30b5\u30a4\u30ba\u3092\u8d85\u3048\u305f\u30c7\u30fc\u30bf\u3067\u306f\u3001\u3042\u3089\u3086\u308b\u30eb\u30fc\u30eb\u3092\u53d6\u308a\u51fa\u3059\u3053\u3068\u306f\u4e0d\u53ef\u80fd\u3068\u3044\u3046\u306e\u304c\u305d\u306e\u7406\u7531\u3067\u3059\u3002\u3067\u3082\u3001\u306a\u308b\u3079\u304f\u591a\u304f\u306e\u30eb\u30fc\u30eb\u3092\u767a\u898b\u3057\u305f\u3044\u3082\u306e\u3067\u3059\u3002\u3053\u308c\u3092\u5b9f\u73fe\u3059\u308b\u305f\u3081\u672c\u7814\u7a76\u3067\u306f\u3001\u4fee\u6b63\u30a2\u30a4\u30c1\u30bd\u30f3-\u30a8\u30a4\u30c8\u30b1\u30f3\u95a2\u6570\u3068\u547c\u3070\u308c\u308b\u96c6\u5408\u3092\u5bfe\u8c61\u3068\u3057\u305f\u30ab\u30fc\u30cd\u30eb\u5bc6\u5ea6\u95a2\u6570\u3092\u63d0\u6848\u3057\u3001\u5f93\u6765\u3088\u308a\u3082\u52b9\u7387\u306e\u826f\u3044\u30d9\u30a4\u30ba\u6700\u9069\u5316\u3092\u751f\u307f\u51fa\u3057\u307e\u3057\u305f\u3002\u3053\u308c\u3092\u30d9\u30fc\u30b9\u3068\u3057\u3001\u6728\u69cb\u9020\u30eb\u30fc\u30eb\u3092\u4f4e\u8a08\u7b97\u30b3\u30b9\u30c8\u3067\u5927\u91cf\u306b\u767a\u898b\u3059\u308b\u30eb\u30fc\u30eb\u30de\u30a4\u30cb\u30f3\u30b0\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u63d0\u6848\u3057\u307e\u3057\u305f\u3002\u672c\u7814\u7a76\u5ba4\u304c\u63d0\u6848\u3057\u305f\u624b\u6cd5\u3092\u7528\u3044\u308b\u3053\u3068\u3067\u3001\u30d3\u30c3\u30b0\u30c7\u30fc\u30bf\u306e\u4e2d\u304b\u3089\u3001\u5f93\u6765\u624b\u6cd5\u3088\u308a\u3082\u591a\u304f\u306e\u30eb\u30fc\u30eb\u3092\u767a\u898b\u3067\u304d\u308b\u3053\u3068\u304c\u78ba\u8a8d\u3055\u308c\u3066\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<a href=\"https:\/\/www.computer.org\/csdl\/journal\/oj\/2024\/01\/10510571\/1WyZbUyrRaU\" rel=\"noopener\" target=\"_blank\">Y. Omae et al., Multi-Rules Mining Algorithm for Combinatorially Exploded Decision Trees With Modified Aitchison-Aitken Function-Based Bayesian Optimization, IEEE Open Journal of the Computer Society, vol.5, pp.215-226, 2024.<\/a>\n<\/li>\n<\/ul>\n<hr>\n<p><strong>\u3088\u308a\u826f\u3044\u7279\u5fb4\u91cf\u63a2\u7d22\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u63d0\u6848\uff1a<\/strong><br \/>\n\u6a5f\u68b0\u5b66\u7fd2\u306b\u3088\u308b\u56de\u5e30\u30fb\u5206\u985e\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3059\u308b\u969b\u3001\u7279\u5fb4\u91cf\u30d9\u30af\u30c8\u30eb\u304c\u9ad8\u6b21\u5143\u3059\u304e\u308b\u3068\u63a8\u5b9a\u6027\u80fd\u304c\u4f4e\u4e0b\u3059\u308b\u3053\u3068\u304c\u77e5\u3089\u308c\u3066\u3044\u307e\u3059\u3002\u305d\u306e\u305f\u3081\u3001\u7279\u5fb4\u91cf\u96c6\u5408\u306e\u4e2d\u304b\u3089\u4e0d\u8981\u306a\u7279\u5fb4\u91cf\u3092\u767a\u898b\u3057\u3001\u9664\u53bb\u3059\u308b\u3053\u3068\u304c\u91cd\u8981\u3067\u3059\u3002\u3057\u304b\u3057\u3001\u7279\u5fb4\u91cf\u540c\u58eb\u306e\u7d44\u307f\u5408\u308f\u305b\u306b\u3088\u308a\u52b9\u679c\u3092\u767a\u63ee\u3059\u308b\u7279\u5fb4\u91cf\u3082\u3042\u308b\u305f\u3081\u3001\u7279\u5fb4\u91cf\u540c\u58eb\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u8003\u616e\u3059\u308b\u3053\u3068\u304c\u5fc5\u8981\u3067\u3059\u3002\u305f\u3060\u3057\u3001\u69cb\u6210\u53ef\u80fd\u306a\u7279\u5fb4\u91cf\u306e\u90e8\u5206\u96c6\u5408\u306f\u7d44\u5408\u305b\u7206\u767a\u3059\u308b\u305f\u3081\u3001\u901a\u5e38\u3001\u5168\u63a2\u7d22\u3092\u884c\u3046\u3053\u3068\u306f\u56f0\u96e3\u3067\u3059\uff08\u898f\u6a21\u304c\u5927\u304d\u3044\u3068\u3001\u30b9\u30d1\u30b3\u30f3\u3067\u3082\u89e3\u3051\u307e\u305b\u3093\uff09\u3002\u3053\u308c\u3092\u89e3\u6c7a\u3059\u308b\u5b66\u8853\u5206\u91ce\u3092\u7279\u5fb4\u91cf\u63a2\u7d22\u3068\u547c\u3073\u3001\u672c\u7814\u7a76\u5ba4\u3067\u3082\u3001\u7279\u5fb4\u91cf\u90e8\u5206\u96c6\u5408\u306e\u8a55\u4fa1\u5024\u3092\u5b9a\u91cf\u5316\u3057\u3001\u5b9f\u73fe\u53ef\u80fd\u306a\u8a08\u7b97\u91cf\u3067\u3088\u308a\u826f\u3044\u7279\u5fb4\u91cf\u306e\u7d44\u307f\u5408\u308f\u305b\u3092\u767a\u898b\u3059\u308b\u624b\u6cd5\u306e\u63d0\u6848\u306b\u53d6\u308a\u7d44\u3093\u3067\u3044\u307e\u3059\u3002<\/p>\n<ul>\n<li>\n<a href=\"https:\/\/www.mdpi.com\/2504-4990\/5\/1\/7\" rel=\"noopener\" target=\"_blank\">Y. Omae et al., E2H Distance-weighted Minimum Reference Set for Numerical and Categorical Mixture Data and a Bayesian Swap Feature Selection Algorithm, Machine Learning and Knowledge Extraction, vol.5, no.1, pp.109-127 2023.<\/a>\n<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u4eca\u3084\u3001\u6a5f\u68b0\u5b66\u7fd2\u306f\u73fe\u5728\u306e\u4e16\u754c\u3092\u652f\u3048\u308b\u57fa\u76e4\u6280\u8853\u306e\u4e00\u3064\u3067\u3059\u3002\u751f\u6210AI\u306e\u767b\u5834\u306b\u3088\u308a\u3001\u5c11\u3057\u52c9\u5f37\u3059\u308b\u3060\u3051\u3067\u3001\u7c21\u5358\u306b\u5b9f\u88c5\u3059\u308b\u3053\u3068\u3082\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3057\u305f\u3002\u672c\u7814\u7a76\u5ba4\u3067\u3001\u3053\u306e\u4e16\u306e\u4e2d\u306e\u6d41\u308c\u3092\u52a0\u901f\u3055\u305b\u308b\u305f\u3081\u306e\u7814\u7a76\u306b\u53d6\u308a\u7d44\u3093\u3067\u3044\u307e\u3059\u3002 \u5b9f\u793e\u4f1a\u3067<\/p><\/div>\n<div class=\"blog-btn\"><a href=\"http:\/\/int-info.com\/index.php\/resv2\/\" class=\"home-blog-btn\">\u7d9a\u304d\u3092\u8aad\u3080<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-4706","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/pages\/4706","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/comments?post=4706"}],"version-history":[{"count":4,"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/pages\/4706\/revisions"}],"predecessor-version":[{"id":4711,"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/pages\/4706\/revisions\/4711"}],"wp:attachment":[{"href":"http:\/\/int-info.com\/index.php\/wp-json\/wp\/v2\/media?parent=4706"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}