Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 80
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W ko = k 1. If the kth member of the training sequence Y is ...
... initial weight vector W1 is arbitrary . We shall be interested here in sequences Sw , which are recursively generated from a training sequence Sy by the following rules : W ko = k 1. If the kth member of the training sequence Y is ...
Sivu 88
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( ) . Yk > W , ( k ) . Yk j = 1 , • 9 R , ji or ( b ) there exists some l = 1 , R , li for which · W ( ) YW , ( k ) • Yk j = 1 , " R ...
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( ) . Yk > W , ( k ) . Yk j = 1 , • 9 R , ji or ( b ) there exists some l = 1 , R , li for which · W ( ) YW , ( k ) • Yk j = 1 , " R ...
Sivu 103
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
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 |