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1 [13] [13] 2 ( ) n-gram ( ) (Google ) [13] (Breiman[3] ) [13] (Friedman[5, 6])
![100 ( ) (Google ) [13]](/docs-images/42/22789527/images/page_1.jpg "(Breiman[3] ) [13]")
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3 MeCab R MeCab RMeCab ( [8] ) 2.4 [13] 0 [9, 10, 11, 12] n-gram
4 [9] [10] [11] n-gram n-gram n n-gram n-gram < > < > < > < > < > < > < > < > < > < > < > < > < > < >N =2 2 2: n-gram(n=2) [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [ ] [13] k Bagging Boosting RandomForest RandomForest Bagging [13]
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5 Boosting (AdaBoost) Boosting Boosting CART CART CART Breiman et al.[1] CART ( [14] ) Boosting Boosting AdaBoost AdaBoost Freund and Schapire[4] Boosting Friedman[5, 6] Boosting Boosting CART Boosting L(y, f(x)) 3 R
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6 3: 1 2 f(x)]2 y f(x) y f(x) sign[y f(x)] 2y 2 log(1 + exp( 2yf(x))) (1+exp(2yf(x))) (2 ) (S S s=1 y s log p s (x) y s p s (x) ) 3.3 Bagging Bagging Breiman[2] Bagging 3.4 RandomForest RandomForest Breiman[3] RandomForest Bagging Bagging ( ) Bagging RandomForest S (S 1,S 2,, 3) (recall) (precision) F i (i =1, 2,,n) A, B
![4 RandomForest RandomForest Breiman[3] RandomForest Bagging Bagging ( ) Bagging](/docs-images/42/22789527/images/page_6.jpg "RandomForest 4 4.1 10 20 5 10 S (S 1,S 2,, 3) 100 4.")
7 G i A R i A A P i A A 4 F (2) a i, b i 0 (3) : R i = a i a i + c i (1) 1:P i = 2:P i = a i a i + b i (2) a i + d i a i + b i + c i + d i (3) : ˆR = 1 n n i=1 F a i a i + c i : ˆP = 1 n n i=1 a i + d i a i + b i + c i + d i (4) F = 2 ˆP ˆR ˆP + ˆR (5) 4: G i A B A a c B b d F 2 ( ) AdaBoost [13] 0 0
8 F F : (2 F ) 2 F 1 AdaBoost Bagging RandomForest RandomForest RandomForest (sfchaos[7] ) 1:9 1:4 F F : ( F )
![RandomForest RandomForest (sfchaos[7] ) 1:9 1:4](/docs-images/42/22789527/images/page_8.jpg "2: ( F )")
9 F 2 RandomForest Bagging AdaBoost RandomForest Bagging AdaBoost RandomForest AdaBoost F 2 2 AdaBoost RandomForest F F : ( F ) 3 2 AdaBoost F Bagging RandomForest RandomForest Bagging AdaBoost AdaBoost Bagging RandomForest F
10 F Bagging AdaBoost RandomForest Bagging RandomForest AdaBoost 2 RandomForest Bagging AdaBoost RandomForest F 0.65 [10] Bagging AdaBoost RandomForest RandomForest Bagging AdaBoost RandomForest Bagging AdaBoost n-gram F n-gram n-gram AdaBoost Bagging RandomForest RandomForest Bagging AdaBoost F RandomForest Bagging F RandomForest F n-gram RandamForest
11 F F : n-gram ( F ) 5 2 AdaBoost Boosting AdaBoost 2 RandamForest RandomForest n-gram n-gram 2 6 RandomForest AdaBoost
12 [1] Breiman, L., Friedman, J. H., Olshen, R. A. & Stone, C. j.(1984): Classification And Regression Trees, Wadsworth. [2] Breiman, L.(1996): Bagging predictors Machine Learning 26(2) [3] Breiman, L.(2001): Random Forests Machine Learning 45(1) [4] Freund, Y. and Schapire, R. E.(1996): Experiments with a new boosting algorithm Machine Learning Proceedings of the Thirteen International Conference [5] Friedman, J. H.(2001): Greedy function approximation: a gradient boosting machine The Annals of Statistics 29(5) [6] Friedman, J. H.(2002): Stochastic gradient boosting: Nonlinear methods and data mining Computational Statistics and Data Analysis [7] sfchaos(2012):, ss [8] (2008) RMeCab, rmecab.jp/wiki/index.php?plugin= attach&refer=rmecab&openfile=manual.pdf. [9] (1993) 46 5 (3) [10] (1996),, 5(2), [11] (2002),, 11(2), [12] (2004),, 32, [13] (2007), 55(2), [14] (2005),, 18(2),
![(1996): Experiments with a new boosting algorithm Machine Learning Proceedings of the Thirteen International Conference 148-156. [5] Friedman, J. H.](/docs-images/42/22789527/images/page_12.jpg "(2001): Greedy function approximation: a gradient boosting machine The Annals of Statistics 29(5) 1189-1232. [6] Friedman, J. H.")
(c) The Institute of Statistical Mathematics 2016
No.118 (2015) 2016 3 190-8562 10-3 (c) The Institute of Statistical Mathematics 2016 No.118 (2015) 2016 3 190-8562 10-3 I 1 1 3 1.1.......................................... 3 1.2........................................
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IS1-09 第 回画像センシングシンポジウム, 横浜,14 年 6 月 MI-Hough Forest () E-mail: ym@vision.cs.chubu.ac.jphf@cs.chubu.ac.jp Abstract Hough Forest Random Forest MI-Hough Forest Multiple Instance Learning Bag Hough Forest
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