Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 24
... respect to point sets . Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each i = 1 , ... , R , let the ith point set consist of the L ; points P. ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean distance d ...
... respect to point sets . Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each i = 1 , ... , R , let the ith point set consist of the L ; points P. ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean distance d ...
Sivu 26
... respect to point sets is a special case . R2 R 1 R1 P ( 1 ) ( 2 ) ( 1 ) P1 = { P , P2 ) } 1 ( 1 ) P2 = { P ) , P ( 2 ) } 2 2 FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The ...
... respect to point sets is a special case . R2 R 1 R1 P ( 1 ) ( 2 ) ( 1 ) P1 = { P , P2 ) } 1 ( 1 ) P2 = { P ) , P ( 2 ) } 2 2 FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The ...
Sivu 135
... respect to point sets , 24 , 121 with respect to points , 16 , 57 Minsky , 62 Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 ...
... respect to point sets , 24 , 121 with respect to points , 16 , 57 Minsky , 62 Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |