Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 117
... bank . Thus , training also in- volves shuffling the subsidiary discriminators from bank to bank until some appropriate organization is found . * What is needed , then , to train PWL machines is a method of adjust- ing weight vectors ...
... bank . Thus , training also in- volves shuffling the subsidiary discriminators from bank to bank until some appropriate organization is found . * What is needed , then , to train PWL machines is a method of adjust- ing weight vectors ...
Sivu 118
... bank , ji , contained the largest subsidiary discriminant . The adjustment method first subtracts Y from the weight vector used by this subsidiary discriminant function in the jth bank . Of those subsidiary discriminant functions in the ...
... bank , ji , contained the largest subsidiary discriminant . The adjustment method first subtracts Y from the weight vector used by this subsidiary discriminant function in the jth bank . Of those subsidiary discriminant functions in the ...
Sivu 122
... banks of weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
... banks of weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category patterns will be ...
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 |