Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... PERCEPTRON and the MADALINE and MINOS networks . The subject of trainable pattern - classifying machines is one aspect of artificial intelligence research about which a growing body of empirical and theoretical knowledge is beginning to ...
... PERCEPTRON and the MADALINE and MINOS networks . The subject of trainable pattern - classifying machines is one aspect of artificial intelligence research about which a growing body of empirical and theoretical knowledge is beginning to ...
Sivu 93
... Perceptron Theory , Cornell Aeronautical Laboratory Report VG - 1196 - G - 7 , Buffalo , New York , June , 1960 . 3 Block , H. D .: The Perceptron : A Model for Brain Functioning , I , Reviews of Modern Physics , vol . 34 , pp . 123-135 ...
... Perceptron Theory , Cornell Aeronautical Laboratory Report VG - 1196 - G - 7 , Buffalo , New York , June , 1960 . 3 Block , H. D .: The Perceptron : A Model for Brain Functioning , I , Reviews of Modern Physics , vol . 34 , pp . 123-135 ...
Sivu 113
... perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of the a perceptron as a three - layer structure because ...
... perceptron proposed by Rosenblatt is a two - layer machine consisting of a first layer of fixed TLUs followed by a single trainable TLU in the second layer . ( Rosenblatt speaks of the a perceptron as a three - layer structure because ...
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 |