Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... modes , 121 7.6 Mode - seeking and related training methods for PWL machines , 122 7.7 Bibliographical and historical remarks , 125 References , 126 APPENDIX A.1 Separation of a quadratic form into positive and negative parts , 127 A.2 ...
... modes , 121 7.6 Mode - seeking and related training methods for PWL machines , 122 7.7 Bibliographical and historical remarks , 125 References , 126 APPENDIX A.1 Separation of a quadratic form into positive and negative parts , 127 A.2 ...
Sivu 122
... be treated separately ; w¿ ‹ denotes the jth weight vector in the ith bank . The problem of estimating modes is generally much more difficult 122 PIECEWISE LINEAR MACHINES Mode-seeking and related training methods for machines,
... be treated separately ; w¿ ‹ denotes the jth weight vector in the ith bank . The problem of estimating modes is generally much more difficult 122 PIECEWISE LINEAR MACHINES Mode-seeking and related training methods for machines,
Sivu 135
... Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 , 93 , 94 Multimodal pattern - classifying tasks , 115 , 116 , 121 Networks ...
... Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 , 93 , 94 Multimodal pattern - classifying tasks , 115 , 116 , 121 Networks ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ 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 |