{"id":477893,"date":"2023-08-09T09:22:01","date_gmt":"2023-08-09T09:22:01","guid":{"rendered":""},"modified":"2023-09-05T11:15:37","modified_gmt":"2023-09-05T11:15:37","slug":"loss-functions","status":"publish","type":"wiki","link":"https:\/\/oneproxy.pro\/jp\/wiki\/loss-functions\/","title":{"rendered":"\u640d\u5931\u95a2\u6570"},"content":{"rendered":"

\u6a5f\u68b0\u5b66\u7fd2\u3068\u4eba\u5de5\u77e5\u80fd\u306e\u5206\u91ce\u3067\u306f\u3001\u640d\u5931\u95a2\u6570\u304c\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u3053\u308c\u3089\u306e\u6570\u5b66\u95a2\u6570\u306f\u3001\u4e88\u6e2c\u3055\u308c\u305f\u51fa\u529b\u3068\u5b9f\u969b\u306e\u771f\u5b9f\u5024\u3068\u306e\u5dee\u3092\u6e2c\u5b9a\u3059\u308b\u305f\u3081\u306e\u3082\u306e\u3067\u3042\u308a\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u304c\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u6700\u9069\u5316\u3057\u3001\u6b63\u78ba\u306a\u4e88\u6e2c\u3092\u884c\u3046\u3053\u3068\u3092\u53ef\u80fd\u306b\u3057\u307e\u3059\u3002\u640d\u5931\u95a2\u6570\u306f\u3001\u56de\u5e30\u3001\u5206\u985e\u3001\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306a\u3069\u3001\u3055\u307e\u3056\u307e\u306a\u30bf\u30b9\u30af\u306b\u4e0d\u53ef\u6b20\u306a\u8981\u7d20\u3067\u3059\u3002<\/p>\n

\u640d\u5931\u95a2\u6570\u306e\u8d77\u6e90\u3068\u305d\u306e\u6700\u521d\u306e\u8a00\u53ca\u306e\u6b74\u53f2\u3002<\/h2>\n

\u640d\u5931\u95a2\u6570\u306e\u6982\u5ff5\u306f\u3001\u7d71\u8a08\u5b66\u3068\u6700\u9069\u5316\u7406\u8ad6\u306e\u521d\u671f\u306e\u9803\u306b\u307e\u3067\u9061\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002\u640d\u5931\u95a2\u6570\u306e\u30eb\u30fc\u30c4\u306f\u300118 \u4e16\u7d00\u3068 19 \u4e16\u7d00\u306e\u30ac\u30a6\u30b9\u3068\u30e9\u30d7\u30e9\u30b9\u306e\u7814\u7a76\u306b\u3042\u308a\u307e\u3059\u3002\u5f7c\u3089\u306f\u3001\u89b3\u6e2c\u5024\u3068\u305d\u306e\u671f\u5f85\u5024\u3068\u306e\u5dee\u306e\u4e8c\u4e57\u548c\u3092\u6700\u5c0f\u5316\u3059\u308b\u3053\u3068\u3092\u76ee\u6307\u3057\u3066\u3001\u6700\u5c0f\u4e8c\u4e57\u6cd5\u3092\u5c0e\u5165\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u6a5f\u68b0\u5b66\u7fd2\u306e\u6587\u8108\u3067\u306f\u3001\u300c\u640d\u5931\u95a2\u6570\u300d\u3068\u3044\u3046\u7528\u8a9e\u306f\u300120 \u4e16\u7d00\u534a\u3070\u306e\u7dda\u5f62\u56de\u5e30\u30e2\u30c7\u30eb\u306e\u958b\u767a\u4e2d\u306b\u6ce8\u76ee\u3092\u96c6\u3081\u307e\u3057\u305f\u3002Abraham Wald \u3068 Ronald Fisher \u306e\u7814\u7a76\u306f\u3001\u7d71\u8a08\u7684\u63a8\u5b9a\u3068\u610f\u601d\u6c7a\u5b9a\u7406\u8ad6\u306b\u304a\u3051\u308b\u640d\u5931\u95a2\u6570\u306e\u7406\u89e3\u3068\u5f62\u5f0f\u5316\u306b\u5927\u304d\u304f\u8ca2\u732e\u3057\u307e\u3057\u305f\u3002<\/p>\n

\u640d\u5931\u95a2\u6570\u306b\u95a2\u3059\u308b\u8a73\u7d30\u60c5\u5831\u3002\u640d\u5931\u95a2\u6570\u306e\u30c8\u30d4\u30c3\u30af\u3092\u62e1\u5f35\u3057\u307e\u3059\u3002<\/h2>\n

\u640d\u5931\u95a2\u6570\u306f\u3001\u6559\u5e2b\u3042\u308a\u5b66\u7fd2\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u306e\u30d0\u30c3\u30af\u30dc\u30fc\u30f3\u3067\u3059\u3002\u640d\u5931\u95a2\u6570\u306f\u3001\u4e88\u6e2c\u5024\u3068\u5b9f\u969b\u306e\u30bf\u30fc\u30b2\u30c3\u30c8\u9593\u306e\u8aa4\u5dee\u307e\u305f\u306f\u5dee\u7570\u3092\u5b9a\u91cf\u5316\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30bb\u30b9\u4e2d\u306b\u30e2\u30c7\u30eb \u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u66f4\u65b0\u3059\u308b\u305f\u3081\u306b\u5fc5\u8981\u306a\u30d5\u30a3\u30fc\u30c9\u30d0\u30c3\u30af\u3092\u63d0\u4f9b\u3057\u307e\u3059\u3002\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3059\u308b\u76ee\u7684\u306f\u3001\u640d\u5931\u95a2\u6570\u3092\u6700\u5c0f\u9650\u306b\u6291\u3048\u3066\u3001\u672a\u77e5\u306e\u30c7\u30fc\u30bf\u306b\u5bfe\u3057\u3066\u6b63\u78ba\u3067\u4fe1\u983c\u6027\u306e\u9ad8\u3044\u4e88\u6e2c\u3092\u5b9f\u73fe\u3059\u308b\u3053\u3068\u3067\u3059\u3002<\/p>\n

\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u3068\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u306e\u30b3\u30f3\u30c6\u30ad\u30b9\u30c8\u3067\u306f\u3001\u640d\u5931\u95a2\u6570\u306f\u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u307e\u3059\u3002\u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3\u3067\u306f\u3001\u52fe\u914d\u304c\u8a08\u7b97\u3055\u308c\u3001\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af \u30ec\u30a4\u30e4\u30fc\u306e\u91cd\u307f\u3092\u66f4\u65b0\u3059\u308b\u305f\u3081\u306b\u5229\u7528\u3055\u308c\u307e\u3059\u3002\u9069\u5207\u306a\u640d\u5931\u95a2\u6570\u306e\u9078\u629e\u306f\u3001\u56de\u5e30\u3084\u5206\u985e\u306a\u3069\u306e\u30bf\u30b9\u30af\u306e\u6027\u8cea\u3068\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7279\u6027\u306b\u3088\u3063\u3066\u7570\u306a\u308a\u307e\u3059\u3002<\/p>\n

\u640d\u5931\u95a2\u6570\u306e\u5185\u90e8\u69cb\u9020\u3002\u640d\u5931\u95a2\u6570\u306e\u4ed5\u7d44\u307f\u3002<\/h2>\n

\u640d\u5931\u95a2\u6570\u306f\u901a\u5e38\u3001\u4e88\u6e2c\u3055\u308c\u305f\u51fa\u529b\u3068\u5b9f\u969b\u306e\u30e9\u30d9\u30eb\u306e\u76f8\u9055\u3092\u6e2c\u5b9a\u3059\u308b\u6570\u5f0f\u306e\u5f62\u3092\u3068\u308a\u307e\u3059\u3002\u5165\u529b (X) \u3068\u5bfe\u5fdc\u3059\u308b\u30bf\u30fc\u30b2\u30c3\u30c8 (Y) \u3092\u542b\u3080\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u304c\u4e0e\u3048\u3089\u308c\u308b\u3068\u3001\u640d\u5931\u95a2\u6570 (L) \u306f\u30e2\u30c7\u30eb\u306e\u4e88\u6e2c (\u0177) \u3092\u30a8\u30e9\u30fc\u3092\u8868\u3059\u5358\u4e00\u306e\u30b9\u30ab\u30e9\u30fc\u5024\u306b\u30de\u30c3\u30d4\u30f3\u30b0\u3057\u307e\u3059\u3002<\/p>\n

L(\u0177, Y)<\/p>\n

\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30d7\u30ed\u30bb\u30b9\u3067\u306f\u3001\u3053\u306e\u30a8\u30e9\u30fc\u3092\u6700\u5c0f\u9650\u306b\u6291\u3048\u308b\u305f\u3081\u306b\u30e2\u30c7\u30eb\u306e\u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u8abf\u6574\u3057\u307e\u3059\u3002\u4e00\u822c\u7684\u306b\u4f7f\u7528\u3055\u308c\u308b\u640d\u5931\u95a2\u6570\u306b\u306f\u3001\u56de\u5e30\u30bf\u30b9\u30af\u306e\u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee (MSE) \u3084\u5206\u985e\u30bf\u30b9\u30af\u306e\u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931\u306a\u3069\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

\u640d\u5931\u95a2\u6570\u306e\u4e3b\u306a\u7279\u5fb4\u306e\u5206\u6790\u3002<\/h2>\n

\u640d\u5931\u95a2\u6570\u306b\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u30b7\u30ca\u30ea\u30aa\u3067\u306e\u4f7f\u7528\u6cd5\u3068\u6709\u52b9\u6027\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u3044\u304f\u3064\u304b\u306e\u91cd\u8981\u306a\u6a5f\u80fd\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n

    \n
  1. \n

    \u9023\u7d9a<\/strong>: \u30b9\u30e0\u30fc\u30ba\u306a\u6700\u9069\u5316\u3092\u53ef\u80fd\u306b\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u4e2d\u306e\u53ce\u675f\u306e\u554f\u984c\u3092\u56de\u907f\u3059\u308b\u306b\u306f\u3001\u640d\u5931\u95a2\u6570\u306f\u9023\u7d9a\u7684\u3067\u3042\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n

  2. \n

    \u5dee\u5225\u5316\u53ef\u80fd\u6027<\/strong>: \u5fae\u5206\u53ef\u80fd\u6027\u306f\u3001\u30d0\u30c3\u30af\u30d7\u30ed\u30d1\u30b2\u30fc\u30b7\u30e7\u30f3 \u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u304c\u52fe\u914d\u3092\u52b9\u7387\u7684\u306b\u8a08\u7b97\u3059\u308b\u305f\u3081\u306b\u975e\u5e38\u306b\u91cd\u8981\u3067\u3059\u3002<\/p>\n<\/li>\n

  3. \n

    \u51f8\u72b6<\/strong>: \u51f8\u640d\u5931\u95a2\u6570\u306b\u306f\u4e00\u610f\u306e\u30b0\u30ed\u30fc\u30d0\u30eb\u6700\u5c0f\u5024\u304c\u3042\u308b\u305f\u3081\u3001\u6700\u9069\u5316\u304c\u3088\u308a\u7c21\u5358\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n

  4. \n

    \u5916\u308c\u5024\u306b\u5bfe\u3059\u308b\u611f\u5ea6<\/strong>\u4e00\u90e8\u306e\u640d\u5931\u95a2\u6570\u306f\u5916\u308c\u5024\u306b\u5bfe\u3057\u3066\u3088\u308a\u654f\u611f\u3067\u3042\u308a\u3001\u30ce\u30a4\u30ba\u306e\u591a\u3044\u30c7\u30fc\u30bf\u304c\u3042\u308b\u5834\u5408\u306b\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u306b\u5f71\u97ff\u3092\u4e0e\u3048\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n

  5. \n

    \u89e3\u91c8\u53ef\u80fd\u6027<\/strong>: \u7279\u5b9a\u306e\u30a2\u30d7\u30ea\u30b1\u30fc\u30b7\u30e7\u30f3\u3067\u306f\u3001\u30e2\u30c7\u30eb\u306e\u52d5\u4f5c\u306b\u95a2\u3059\u308b\u6d1e\u5bdf\u3092\u5f97\u308b\u305f\u3081\u306b\u3001\u89e3\u91c8\u53ef\u80fd\u306a\u640d\u5931\u95a2\u6570\u304c\u597d\u307e\u308c\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

    \u640d\u5931\u95a2\u6570\u306e\u7a2e\u985e<\/h2>\n

    \u640d\u5931\u95a2\u6570\u306b\u306f\u3055\u307e\u3056\u307e\u306a\u7a2e\u985e\u304c\u3042\u308a\u3001\u305d\u308c\u305e\u308c\u7279\u5b9a\u306e\u6a5f\u68b0\u5b66\u7fd2\u30bf\u30b9\u30af\u306b\u9069\u3057\u3066\u3044\u307e\u3059\u3002\u640d\u5931\u95a2\u6570\u306e\u4e00\u822c\u7684\u306a\u7a2e\u985e\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n\n\n\n\n\n\n\n\n\n
    \u640d\u5931\u95a2\u6570<\/th>\n\u30bf\u30b9\u30af\u306e\u7a2e\u985e<\/th>\n\u5f0f<\/th>\n<\/tr>\n<\/thead>\n
    \u5e73\u5747\u4e8c\u4e57\u8aa4\u5dee<\/td>\n\u56de\u5e30<\/td>\nMSE(\u0177, Y) = (1\/n) \u03a3(\u0177 \u2013 Y)^2<\/td>\n<\/tr>\n
    \u30af\u30ed\u30b9\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u640d\u5931<\/td>\n\u5206\u985e<\/td>\nCE(\u0177, Y) = -\u03a3(Y * log(\u0177) + (1 \u2013 Y) * log(1 \u2013 \u0177))<\/td>\n<\/tr>\n
    \u30d2\u30f3\u30b8\u640d\u5931<\/td>\n\u30b5\u30dd\u30fc\u30c8\u30d9\u30af\u30bf\u30fc\u30de\u30b7\u30f3<\/td>\nHL(\u0177, Y) = \u6700\u5927\u5024(0, 1 \u2013 \u0177 * Y)<\/td>\n<\/tr>\n
    \u30d5\u30fc\u30d0\u30fc\u30ed\u30b9<\/td>\n\u30ed\u30d0\u30b9\u30c8\u56de\u5e30<\/td>\nHL(\u0177, Y) = { 0.5 * (\u0177 \u2013 Y)^2 \u306e\u5834\u5408<\/td>\n<\/tr>\n
    \u30c0\u30a4\u30b9\u306e\u640d\u5931<\/td>\n\u753b\u50cf\u30bb\u30b0\u30e1\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3<\/td>\nDL(\u0177, Y) = 1 \u2013 (2 * \u03a3(\u0177 * Y) + \u025b) \/ (\u03a3\u0177 + \u03a3Y + \u025b)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u640d\u5931\u95a2\u6570\u306e\u4f7f\u3044\u65b9\u3001\u4f7f\u7528\u4e0a\u306e\u554f\u984c\u70b9\u3068\u305d\u306e\u89e3\u6c7a\u7b56\u3002<\/h2>\n

    \u9069\u5207\u306a\u640d\u5931\u95a2\u6570\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u306f\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u6210\u529f\u306b\u3068\u3063\u3066\u91cd\u8981\u3067\u3059\u3002\u305f\u3060\u3057\u3001\u9069\u5207\u306a\u640d\u5931\u95a2\u6570\u3092\u9078\u629e\u3059\u308b\u3053\u3068\u306f\u96e3\u3057\u3044\u5834\u5408\u304c\u3042\u308a\u3001\u30c7\u30fc\u30bf\u306e\u6027\u8cea\u3001\u30e2\u30c7\u30eb \u30a2\u30fc\u30ad\u30c6\u30af\u30c1\u30e3\u3001\u76ee\u7684\u306e\u51fa\u529b\u306a\u3069\u306e\u8981\u56e0\u306b\u3088\u3063\u3066\u7570\u306a\u308a\u307e\u3059\u3002<\/p>\n

    \u8ab2\u984c:<\/strong><\/p>\n

      \n
    1. \n

      \u968e\u7d1a\u306e\u4e0d\u5747\u8861<\/strong>: \u5206\u985e\u30bf\u30b9\u30af\u3067\u306f\u3001\u30af\u30e9\u30b9\u5206\u5e03\u306e\u4e0d\u5747\u8861\u306b\u3088\u308a\u504f\u3063\u305f\u30e2\u30c7\u30eb\u304c\u751f\u6210\u3055\u308c\u308b\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u306e\u554f\u984c\u306b\u5bfe\u51e6\u3059\u308b\u306b\u306f\u3001\u52a0\u91cd\u640d\u5931\u95a2\u6570\u3084\u3001\u30aa\u30fc\u30d0\u30fc\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u3084\u30a2\u30f3\u30c0\u30fc\u30b5\u30f3\u30d7\u30ea\u30f3\u30b0\u306a\u3069\u306e\u624b\u6cd5\u3092\u4f7f\u7528\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

    2. \n

      \u904e\u5b66\u7fd2<\/strong>: \u4e00\u90e8\u306e\u640d\u5931\u95a2\u6570\u306f\u904e\u5270\u9069\u5408\u3092\u60aa\u5316\u3055\u305b\u3001\u4e00\u822c\u5316\u304c\u4e0d\u5341\u5206\u306b\u306a\u308b\u53ef\u80fd\u6027\u304c\u3042\u308a\u307e\u3059\u3002L1 \u6b63\u5247\u5316\u3084 L2 \u6b63\u5247\u5316\u306a\u3069\u306e\u6b63\u5247\u5316\u624b\u6cd5\u306f\u3001\u904e\u5270\u9069\u5408\u3092\u8efd\u6e1b\u3059\u308b\u306e\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n<\/li>\n

    3. \n

      \u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb\u30c7\u30fc\u30bf<\/strong>: \u30de\u30eb\u30c1\u30e2\u30fc\u30c0\u30eb \u30c7\u30fc\u30bf\u3092\u6271\u3046\u5834\u5408\u3001\u8907\u6570\u306e\u6700\u9069\u89e3\u304c\u3042\u308b\u305f\u3081\u306b\u30e2\u30c7\u30eb\u306e\u53ce\u675f\u304c\u56f0\u96e3\u306b\u306a\u308b\u3053\u3068\u304c\u3042\u308a\u307e\u3059\u3002\u30ab\u30b9\u30bf\u30e0\u640d\u5931\u95a2\u6570\u307e\u305f\u306f\u751f\u6210\u30e2\u30c7\u30eb\u3092\u691c\u8a0e\u3059\u308b\u3068\u5f79\u7acb\u3064\u5834\u5408\u304c\u3042\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

      \u89e3\u6c7a\u7b56:<\/strong><\/p>\n

        \n
      1. \n

        \u30ab\u30b9\u30bf\u30e0\u640d\u5931\u95a2\u6570<\/strong>\u30bf\u30b9\u30af\u56fa\u6709\u306e\u640d\u5931\u95a2\u6570\u3092\u8a2d\u8a08\u3059\u308b\u3053\u3068\u3067\u3001\u7279\u5b9a\u306e\u8981\u4ef6\u3092\u6e80\u305f\u3059\u3088\u3046\u306b\u30e2\u30c7\u30eb\u306e\u52d5\u4f5c\u3092\u8abf\u6574\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n

      2. \n

        \u30e1\u30c8\u30ea\u30c3\u30af\u5b66\u7fd2<\/strong>\u76f4\u63a5\u7684\u306a\u76e3\u7763\u304c\u5236\u9650\u3055\u308c\u3066\u3044\u308b\u30b7\u30ca\u30ea\u30aa\u3067\u306f\u3001\u30e1\u30c8\u30ea\u30c3\u30af\u5b66\u7fd2\u640d\u5931\u95a2\u6570\u3092\u4f7f\u7528\u3057\u3066\u3001\u30b5\u30f3\u30d7\u30eb\u9593\u306e\u985e\u4f3c\u6027\u307e\u305f\u306f\u8ddd\u96e2\u3092\u5b66\u7fd2\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n

      3. \n

        \u9069\u5fdc\u640d\u5931\u95a2\u6570<\/strong>: \u7126\u70b9\u640d\u5931\u306a\u3069\u306e\u624b\u6cd5\u3067\u306f\u3001\u500b\u3005\u306e\u30b5\u30f3\u30d7\u30eb\u306e\u96e3\u6613\u5ea6\u306b\u57fa\u3065\u3044\u3066\u640d\u5931\u306e\u91cd\u307f\u3092\u8abf\u6574\u3057\u3001\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u4e2d\u306b\u96e3\u3057\u3044\u4f8b\u3092\u512a\u5148\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

        \u4e3b\u306a\u7279\u5fb4\u3084\u305d\u306e\u4ed6\u306e\u985e\u4f3c\u7528\u8a9e\u3068\u306e\u6bd4\u8f03\u3092\u8868\u3084\u30ea\u30b9\u30c8\u306e\u5f62\u5f0f\u3067\u793a\u3057\u307e\u3059\u3002<\/h2>\n\n\n\n\n\n\n\n\n\n\n
        \u5b66\u671f<\/th>\n\u8aac\u660e<\/th>\n<\/tr>\n<\/thead>\n
        \u640d\u5931\u95a2\u6570<\/td>\n\u6a5f\u68b0\u5b66\u7fd2\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u306b\u304a\u3051\u308b\u4e88\u6e2c\u5024\u3068\u5b9f\u969b\u306e\u5024\u306e\u5dee\u7570\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002<\/td>\n<\/tr>\n
        \u30b3\u30b9\u30c8\u95a2\u6570<\/td>\n\u6700\u9069\u306a\u30e2\u30c7\u30eb \u30d1\u30e9\u30e1\u30fc\u30bf\u3092\u898b\u3064\u3051\u308b\u305f\u3081\u306e\u6700\u9069\u5316\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3067\u4f7f\u7528\u3055\u308c\u307e\u3059\u3002<\/td>\n<\/tr>\n
        \u76ee\u7684\u95a2\u6570<\/td>\n\u6a5f\u68b0\u5b66\u7fd2\u30bf\u30b9\u30af\u3067\u6700\u9069\u5316\u3055\u308c\u308b\u76ee\u6a19\u3092\u8868\u3057\u307e\u3059\u3002<\/td>\n<\/tr>\n
        \u6b63\u898f\u5316\u640d\u5931<\/td>\n\u5927\u304d\u306a\u30d1\u30e9\u30e1\u30fc\u30bf\u5024\u3092\u63a8\u5968\u3057\u306a\u3044\u3053\u3068\u3067\u904e\u5270\u9069\u5408\u3092\u9632\u3050\u305f\u3081\u306e\u8ffd\u52a0\u306e\u30da\u30ca\u30eb\u30c6\u30a3\u9805\u3002<\/td>\n<\/tr>\n
        \u7d4c\u9a13\u7684\u30ea\u30b9\u30af<\/td>\n\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0 \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3067\u8a08\u7b97\u3055\u308c\u305f\u5e73\u5747\u640d\u5931\u95a2\u6570\u5024\u3002<\/td>\n<\/tr>\n
        \u60c5\u5831\u306e\u7372\u5f97<\/td>\n\u6c7a\u5b9a\u6728\u3067\u306f\u3001\u7279\u5b9a\u306e\u5c5e\u6027\u306b\u3088\u308b\u30a8\u30f3\u30c8\u30ed\u30d4\u30fc\u306e\u6e1b\u5c11\u3092\u6e2c\u5b9a\u3057\u307e\u3059\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

        \u640d\u5931\u95a2\u6570\u306b\u95a2\u3059\u308b\u5c06\u6765\u306e\u5c55\u671b\u3068\u6280\u8853\u3002<\/h2>\n

        \u6a5f\u68b0\u5b66\u7fd2\u3068\u4eba\u5de5\u77e5\u80fd\u304c\u9032\u5316\u3057\u7d9a\u3051\u308b\u306b\u3064\u308c\u3066\u3001\u640d\u5931\u95a2\u6570\u306e\u958b\u767a\u3068\u6539\u826f\u3082\u9032\u307f\u307e\u3059\u3002\u5c06\u6765\u306e\u5c55\u671b\u3068\u3057\u3066\u306f\u3001\u6b21\u306e\u3088\u3046\u306a\u3053\u3068\u304c\u8003\u3048\u3089\u308c\u307e\u3059\u3002<\/p>\n

          \n
        1. \n

          \u9069\u5fdc\u640d\u5931\u95a2\u6570<\/strong>: \u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u4e2d\u306b\u640d\u5931\u95a2\u6570\u3092\u81ea\u52d5\u7684\u306b\u9069\u5fdc\u3055\u305b\u3066\u3001\u7279\u5b9a\u306e\u30c7\u30fc\u30bf\u5206\u5e03\u3067\u306e\u30e2\u30c7\u30eb\u306e\u30d1\u30d5\u30a9\u30fc\u30de\u30f3\u30b9\u3092\u5411\u4e0a\u3055\u305b\u307e\u3059\u3002<\/p>\n<\/li>\n

        2. \n

          \u4e0d\u78ba\u5b9f\u6027\u3092\u8003\u616e\u3057\u305f\u640d\u5931\u95a2\u6570<\/strong>: \u640d\u5931\u95a2\u6570\u306b\u4e0d\u78ba\u5b9f\u6027\u306e\u63a8\u5b9a\u3092\u5c0e\u5165\u3057\u3066\u3001\u3042\u3044\u307e\u3044\u306a\u30c7\u30fc\u30bf \u30dd\u30a4\u30f3\u30c8\u3092\u52b9\u679c\u7684\u306b\u51e6\u7406\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

        3. \n

          \u5f37\u5316\u5b66\u7fd2\u640d\u5931<\/strong>\u5f37\u5316\u5b66\u7fd2\u6280\u8853\u3092\u7d44\u307f\u8fbc\u3093\u3067\u3001\u9806\u6b21\u610f\u601d\u6c7a\u5b9a\u30bf\u30b9\u30af\u306e\u30e2\u30c7\u30eb\u3092\u6700\u9069\u5316\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

        4. \n

          \u30c9\u30e1\u30a4\u30f3\u56fa\u6709\u306e\u640d\u5931\u95a2\u6570<\/strong>: \u640d\u5931\u95a2\u6570\u3092\u7279\u5b9a\u306e\u30c9\u30e1\u30a4\u30f3\u306b\u5408\u308f\u305b\u3066\u8abf\u6574\u3059\u308b\u3053\u3068\u3067\u3001\u3088\u308a\u52b9\u7387\u7684\u3067\u6b63\u78ba\u306a\u30e2\u30c7\u30eb\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u304c\u53ef\u80fd\u306b\u306a\u308a\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

          \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u3092\u3069\u306e\u3088\u3046\u306b\u4f7f\u7528\u3057\u3001\u640d\u5931\u95a2\u6570\u3068\u3069\u306e\u3088\u3046\u306b\u95a2\u9023\u4ed8\u3051\u308b\u304b\u3002<\/h2>\n

          \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u6a5f\u68b0\u5b66\u7fd2\u306e\u3055\u307e\u3056\u307e\u306a\u5074\u9762\u3067\u91cd\u8981\u306a\u5f79\u5272\u3092\u679c\u305f\u3057\u3066\u304a\u308a\u3001\u640d\u5931\u95a2\u6570\u3068\u306e\u95a2\u9023\u306f\u3044\u304f\u3064\u304b\u306e\u30b7\u30ca\u30ea\u30aa\u3067\u78ba\u8a8d\u3067\u304d\u307e\u3059\u3002<\/p>\n

            \n
          1. \n

            \u30c7\u30fc\u30bf\u53ce\u96c6<\/strong>: \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u30c7\u30fc\u30bf\u53ce\u96c6\u8981\u6c42\u3092\u533f\u540d\u5316\u3057\u3066\u5206\u6563\u3059\u308b\u305f\u3081\u306b\u4f7f\u7528\u3067\u304d\u3001\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u7528\u306e\u591a\u69d8\u3067\u504f\u308a\u306e\u306a\u3044\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u69cb\u7bc9\u306b\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n<\/li>\n

          2. \n

            \u30c7\u30fc\u30bf\u62e1\u5f35<\/strong>\u30d7\u30ed\u30ad\u30b7\u306f\u3001\u3055\u307e\u3056\u307e\u306a\u5730\u7406\u7684\u306a\u5834\u6240\u304b\u3089\u30c7\u30fc\u30bf\u3092\u53ce\u96c6\u3057\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u5145\u5b9f\u3055\u305b\u3001\u904e\u5270\u9069\u5408\u3092\u6e1b\u3089\u3059\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf\u62e1\u5f35\u3092\u5bb9\u6613\u306b\u3059\u308b\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n<\/li>\n

          3. \n

            \u30d7\u30e9\u30a4\u30d0\u30b7\u30fc\u3068\u30bb\u30ad\u30e5\u30ea\u30c6\u30a3<\/strong>: \u30d7\u30ed\u30ad\u30b7\u306f\u3001\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u4e2d\u306b\u6a5f\u5bc6\u60c5\u5831\u3092\u4fdd\u8b77\u3057\u3001\u30c7\u30fc\u30bf\u4fdd\u8b77\u898f\u5236\u3078\u306e\u6e96\u62e0\u3092\u4fdd\u8a3c\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n

          4. \n

            \u30e2\u30c7\u30eb\u306e\u5c55\u958b<\/strong>: \u30d7\u30ed\u30ad\u30b7 \u30b5\u30fc\u30d0\u30fc\u306f\u3001\u8ca0\u8377\u5206\u6563\u3068\u30e2\u30c7\u30eb\u4e88\u6e2c\u306e\u5206\u6563\u3092\u652f\u63f4\u3057\u3001\u52b9\u7387\u7684\u3067\u30b9\u30b1\u30fc\u30e9\u30d6\u30eb\u306a\u5c55\u958b\u3092\u4fdd\u8a3c\u3057\u307e\u3059\u3002<\/p>\n<\/li>\n<\/ol>\n

            \u95a2\u9023\u30ea\u30f3\u30af<\/h2>\n

            \u640d\u5931\u95a2\u6570\u3068\u305d\u306e\u5fdc\u7528\u306b\u95a2\u3059\u308b\u8a73\u7d30\u306b\u3064\u3044\u3066\u306f\u3001\u6b21\u306e\u30ea\u30bd\u30fc\u30b9\u304c\u5f79\u7acb\u3061\u307e\u3059\u3002<\/p>\n

              \n
            1. \u30b9\u30bf\u30f3\u30d5\u30a9\u30fc\u30c9 CS231n: \u8996\u899a\u8a8d\u8b58\u306e\u305f\u3081\u306e\u7573\u307f\u8fbc\u307f\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af<\/a><\/li>\n
            2. \u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0\u306e\u672c: \u7b2c 5 \u7ae0\u3001\u30cb\u30e5\u30fc\u30e9\u30eb \u30cd\u30c3\u30c8\u30ef\u30fc\u30af\u3068\u30c7\u30a3\u30fc\u30d7\u30e9\u30fc\u30cb\u30f3\u30b0<\/a><\/li>\n
            3. Scikit-learn \u30c9\u30ad\u30e5\u30e1\u30f3\u30c8: \u640d\u5931\u95a2\u6570<\/a><\/li>\n
            4. \u30c7\u30fc\u30bf\u30b5\u30a4\u30a8\u30f3\u30b9\u306b\u5411\u3051\u3066: \u640d\u5931\u95a2\u6570\u3092\u7406\u89e3\u3059\u308b<\/a><\/li>\n<\/ol>\n

              \u6a5f\u68b0\u5b66\u7fd2\u3068 AI \u304c\u9032\u6b69\u3057\u7d9a\u3051\u308b\u306b\u3064\u308c\u3066\u3001\u640d\u5931\u95a2\u6570\u306f\u30e2\u30c7\u30eb\u306e\u30c8\u30ec\u30fc\u30cb\u30f3\u30b0\u3068\u6700\u9069\u5316\u306b\u304a\u3044\u3066\u91cd\u8981\u306a\u8981\u7d20\u3067\u3042\u308a\u7d9a\u3051\u307e\u3059\u3002\u3055\u307e\u3056\u307e\u306a\u7a2e\u985e\u306e\u640d\u5931\u95a2\u6570\u3068\u305d\u306e\u5fdc\u7528\u3092\u7406\u89e3\u3059\u308b\u3053\u3068\u3067\u3001\u30c7\u30fc\u30bf \u30b5\u30a4\u30a8\u30f3\u30c6\u30a3\u30b9\u30c8\u3084\u7814\u7a76\u8005\u306f\u3001\u3088\u308a\u5805\u7262\u3067\u6b63\u78ba\u306a\u6a5f\u68b0\u5b66\u7fd2\u30e2\u30c7\u30eb\u3092\u69cb\u7bc9\u3057\u3001\u73fe\u5b9f\u4e16\u754c\u306e\u8ab2\u984c\u306b\u53d6\u308a\u7d44\u3080\u3053\u3068\u304c\u3067\u304d\u308b\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/p>","protected":false},"featured_media":468810,"menu_order":0,"template":"","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"class_list":["post-477893","wiki","type-wiki","status-publish","has-post-thumbnail","hentry"],"acf":{"faq_title":"Frequently Asked Questions about Loss functions: Understanding the Crucial Element in Machine Learning<\/mark>","faq_items":[{"question":"What are Loss functions, and why are they important in machine learning?","answer":"

              Loss functions are mathematical tools that measure the difference between predicted outputs and actual ground truth values in machine learning models. They play a crucial role in training algorithms, enabling models to optimize their parameters and make accurate predictions. By minimizing the loss function, models can achieve better performance on unseen data and solve various tasks, including regression and classification.<\/p>"},{"question":"How did Loss functions originate, and who first mentioned them?","answer":"

              The concept of loss functions can be traced back to the works of Gauss and Laplace in the 18th and 19th centuries, where they introduced the method of least squares to minimize the squared differences between observations and their expected values. In the context of machine learning, the term \"loss function\" gained prominence during the development of linear regression models in the mid-20th century. Abraham Wald and Ronald Fisher significantly contributed to the formalization of loss functions in statistical estimation and decision theory.<\/p>"},{"question":"What is the internal structure of Loss functions, and how do they work?","answer":"

              Loss functions are mathematical equations that measure the dissimilarity between predicted outputs and ground truth labels. Given a dataset with inputs and corresponding targets, a loss function maps the predictions of a model to a single scalar value representing the error. During training, the model adjusts its parameters to minimize this error, which is critical in backpropagation for neural network training.<\/p>"},{"question":"What are the main types of Loss functions, and when are they used?","answer":"

              There are various types of loss functions, each suited for specific machine learning tasks. Common ones include Mean Squared Error (MSE) for regression, Cross-Entropy Loss for classification, Hinge Loss for support vector machines, Huber Loss for robust regression, and Dice Loss for image segmentation.<\/p>"},{"question":"What are the key features of Loss functions, and how do they impact model training?","answer":"

              Loss functions possess essential characteristics, including continuity, differentiability, convexity, sensitivity to outliers, and interpretability. These features influence the model's optimization process, convergence, and generalization performance.<\/p>"},{"question":"What are the challenges related to using Loss functions, and how can they be addressed?","answer":"

              Challenges in using loss functions include dealing with class imbalance, overfitting, and multimodal data. Addressing these challenges may involve techniques such as weighted loss functions, regularization, custom loss designs, and metric learning.<\/p>"},{"question":"How can Loss functions evolve in the future of machine learning?","answer":"

              Future perspectives for Loss functions include adaptive loss functions that adjust during training, uncertainty-aware loss functions, reinforcement learning losses for sequential decision-making, and domain-specific loss functions tailored to specific applications.<\/p>"},{"question":"How are proxy servers associated with Loss functions and machine learning?","answer":"

              Proxy servers play a significant role in machine learning by aiding in data collection, data augmentation, privacy, security, and model deployment. They enable researchers and data scientists to build more diverse and robust machine learning models.<\/p>"},{"question":"Where can I find more information about Loss functions?","answer":"

              For more in-depth information about Loss functions and their applications, you can explore resources such as Stanford CS231n, Deep Learning Book's Chapter 5, Scikit-learn Documentation, and Towards Data Science articles on understanding loss functions. Additionally, OneProxy, the leading proxy server provider, offers valuable insights into the connection between Loss functions and their cutting-edge technologies.<\/p>"}]},"_links":{"self":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477893","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki"}],"about":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/types\/wiki"}],"version-history":[{"count":0,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/wiki\/477893\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media\/468810"}],"wp:attachment":[{"href":"https:\/\/oneproxy.pro\/jp\/wp-json\/wp\/v2\/media?parent=477893"}],"curies":[{"name":"\u3046\u30fc\u3093","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}