{"id":3339,"date":"2019-01-30T05:14:57","date_gmt":"2019-01-29T20:14:57","guid":{"rendered":"https:\/\/aimap.imi.kyushu-u.ac.jp\/wp\/?post_type=tribe_events&p=3339"},"modified":"2019-03-14T18:58:28","modified_gmt":"2019-03-14T09:58:28","slug":"2018a006","status":"publish","type":"tribe_events","link":"https:\/\/aimap.imi.kyushu-u.ac.jp\/wp\/event\/2018a006\/","title":{"rendered":"RIMS\u5171\u540c\u7814\u7a76\uff08\u30b0\u30eb\u30fc\u30d7\u578b\uff09\u6700\u5c24\u6cd5\u3068\u30d9\u30a4\u30ba\u6cd5"},"content":{"rendered":"

\u25a0\u958b\u50ac\u65e5\u6642\uff1a2019 \u5e743 \u67086 \u65e5\uff08\u6c34\uff0913:35 ~ 3 \u67088 \u65e5\uff08\u91d1\uff0912:00
\n\u25a0\u958b\u50ac\u5834\u6240\uff1a\u4eac\u90fd\u5927\u5b66\u6570\u7406\u89e3\u6790\u7814\u7a76\u6240111\u53f7\u5ba4
\n\u4eac\u90fd\u5e02\u5de6\u4eac\u533a\u5317\u767d\u5ddd\u8ffd\u5206\u753a\uff0c\u5e02\u30d0\u30b9\u4eac\u5927\u8fb2\u5b66\u90e8\u524d\u307e\u305f\u306f\u5317\u767d\u5ddd\u4e0b\u8eca
\n\u30a2\u30af\u30bb\u30b9\u3054\u53c2\u7167\u304f\u3060\u3055\u3044\uff0e http:\/\/www.kurims.kyoto-u.ac.jp\/ja\/access-01.htm<\/a><\/span>
\n\u25a0\u7814\u7a76\u4ee3\u8868\u8005\uff1a\u5c0f\u6c60\u3000\u5065\u4e00\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09
\n\u25a0\u3010\u4e3b\u50ac\u3011RIMS \u5171\u540c\u7814\u7a76\uff08\u30b0\u30eb\u30fc\u30d7\u578b\uff09
\n\u3000\u3010\u5171\u50ac\u3011\u6587\u90e8\u79d1\u5b66\u7701\u59d4\u8a17\u4e8b\u696d\u300c\u6570\u5b66\u30a2\u30c9\u30d0\u30f3\u30b9\u30c8\u30a4\u30ce\u30d9\u30fc\u30b7\u30e7\u30f3\u30d7\u30e9\u30c3\u30c8\u30d5\u30a9\u30fc\u30e0(AIMaP)\u300d<\/p>\n

\u30d7\u30ed\u30b0\u30e9\u30e0<\/strong>\r\n3\u67086\u65e5\uff08\u6c34\uff09\r\n13:35 \u958b\u4f1a\r\n13:40~14:20 \u5c0f\u6c60\u5065\u4e00\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09\r\nAsymptotic comparison of Bayesian inequalities\r\n14:30 ~ 15:10 \u5d0e\u6ff1\u6804\u6cbb\uff08\uff08\u682a\uff09\u30d5\u30a1\u30f3\u30b3\u30df\u30e5\u30cb\u30b1\u30fc\u30b7\u30e7\u30f3\u30ba\u30fb\u60c5\u5831\u79d1\u5b66\u6280\u8853\u7814\u7a76\u6240\uff09\u9e7f\u5cf6\u6d69\u4e4b\uff08\u9752\u5c71\u5b66\u9662\u5927\u30fb\u7d4c\u55b6\u5b66\u90e8\uff09\r\nLatent Dirichlet Allocation \u3092\u6d3b\u7528\u3057\u305f\u30ec\u30d3\u30e5\u30fc\u8a18\u4e8b\u306e\u4fe1\u983c\u6027\u5224\u65ad\u624b\u6cd5\u306b\u3064\u3044\u3066\r\n15:20 ~ 16:00 \u5bae\u7530\u5eb8\u4e00\uff08\u9ad8\u5d0e\u7d4c\u6e08\u5927\u30fb\u7d4c\u6e08\uff09\r\n\u5869\u6ff1\u656c\u4e4b\uff08\u6771\u4eac\u7406\u79d1\u5927\u30fb\u5de5\uff09\u963f\u90e8\u4fca\u5f18\uff08\u5357\u5c71\u5927\u30fb\u7406\u5de5\uff09\r\n\u6b63\u5f26\u95a2\u6570\u306b\u57fa\u3065\u304f\u975e\u5bfe\u79f0\u306a\u5186\u5468\u5206\u5e03\u306e\u63a8\u5b9a\u7406\u8ad6\u306b\u304a\u3051\u308b\u8af8\u554f\u984c\u306b\u3064\u3044\u3066\r\n3\u67087\u65e5\uff08\u6728\uff09\r\n9:30 ~ 10:10 \u4e2d\u5c71\u512a\u543e\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\u30fb\u9662\uff09\u77e2\u7530\u548c\u5584\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09\u9752\u5d8b\u3000\u8aa0\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09\r\n\u30ab\u30fc\u30cd\u30eb\u4e3b\u6210\u5206\u5206\u6790\u306b\u57fa\u3065\u304f\u9ad8\u6b21\u5143\u30c7\u30fc\u30bf\u306e\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306b\u3064\u3044\u3066<\/span>\r\n10:20 ~ 11:00 \u77f3\u4e95\u3000\u6676\uff08\u6771\u4eac\u7406\u79d1\u5927\u30fb\u7406\u5de5\uff09\r\n\u77e2\u7530\u548c\u5584\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09\u9752\u5d8b\u3000\u8aa0\uff08\u7b51\u6ce2\u5927\u30fb\u6570\u7406\u7269\u8cea\uff09\r\n\u5f37\u30b9\u30d1\u30a4\u30af\u30e2\u30c7\u30eb\u306b\u304a\u3051\u308b\u56fa\u6709\u7a7a\u9593\u306e\u63a8\u6e2c\u3068\u9ad8\u6b21\u5143\u5e73\u5747\u30d9\u30af\u30c8\u30eb\u306e\u691c\u5b9a\r\n11:10 ~ 11:50 \u6801\u539f\u5b8f\u548c\uff08\u5e83\u5cf6\u5927\u30fb\u7406\uff09<\/span>\r\nHigh-dimensionality Adjusted Asymptotically Loss Efficient GCp Criterion in Normal Multivariate Linear Regression Models\r\n13:20 ~ 13:50 \u85ae\u91ce\u6469\u5468\uff08\u5927\u962a\u5e9c\u7acb\u5927\u30fb\u7406\u30fb\u9662\uff09<\/span>\r\n\u30d1\u30ec\u30fc\u30c8\u5206\u5e03\u306e\u5f62\u72b6\u6bcd\u6570\u306e\u63a8\u5b9a\u3068\u6bd4\u8f03 \r\n14:00 ~ 14:40 \u7530\u4e2d\u7814\u592a\u90ce\uff08\u6210\u8e4a\u5927\u30fb\u7d4c\u6e08\uff09\r\n\u5b9f\u9a13\u8a08\u753b\u6cd5\u306b\u304a\u3051\u308b\u8a08\u753b\u884c\u5217\u306e\u751f\u6210\u306b\u3064\u3044\u3066\r\n14:50 ~ 15:30 \u6e05\u3000\u667a\u4e5f\uff08\u6771\u4eac\u5927\u30fb\u60c5\u5831\u7406\u5de5\uff09\r\nA possible extension of regression analysis for imbalanced binary data<\/span>\r\n15:40 ~ 16:20 \u6a2a\u5c71\u96c5\u4e4b\uff08\u6838\u878d\u5408\u79d1\u5b66\u7814\u7a76\u6240\uff09\r\n\u30c7\u30fc\u30bf\u99c6\u52d5\u624b\u6cd5\u306b\u3088\u308b\u6838\u878d\u5408\u30d7\u30e9\u30ba\u30de\u306e\u71b1\u8f38\u9001\u30e2\u30c7\u30ea\u30f3\u30b0\u306e\u8a66\u307f\r\n3\u67088\u65e5\uff08\u91d1\uff09\r\n9:30 ~ 10:10 \u5f35\u3000\u5143\u5b97\uff08\u76ee\u767d\u5927\u30fb\u793e\u4f1a\uff09<\/span>\r\n\u7be0\u5d0e\u4fe1\u96c4\uff08\u6176\u61c9\u5927\u30fb\u7406\u5de5\uff09\r\n\u5236\u7d04\u6761\u4ef6\u304c\u3042\u308b\u5834\u5408\u306e\u6b63\u898f\u6bcd\u5e73\u5747\u306e\u6700\u5c24\u63a8\u5b9a\u91cf\u3068\u4e00\u822c\u30d9\u30a4\u30ba\u63a8\u5b9a\u91cf\r\n10:20 ~ 11:00 \u98db\u7530\u82f1\u7950\uff08\u5927\u962a\u5927\u30fb\u533b\uff09\r\nReal World Data \u3092\u7528\u3044\u305f\u975e\u52a3\u6027\u306e\u691c\u8a3c\u306b\u3064\u3044\u3066<\/span>\r\n11:10 ~ 11:50 \u91d1\u6fa4\u96c4\u4e00\u90ce\uff08\u56fd\u969b\u57fa\u7763\u6559\u5927\u30fb\u6559\u990a\uff09\r\nHow to quantify the distance between two 3-dimensionally esti-mated objects?: A trustworthiness comparison between Canada and Japan\r\n11:55 \u9589\u4f1a               \u3000\u3000\u3000\u3000\u3000\u3000\u3000\u3000\uff082019\/2\/8<\/span>\uff09\r\n<\/em><\/span><\/span><\/pre>\n

\u25a0\u63a1\u629e\u756a\u53f7\uff1a2018A006<\/p>\n

\u30d7\u30ed\u30b0\u30e9\u30e0(PDF)<\/a><\/p>\n

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