Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of a two - category ...
... element whose threshold value is equal to zero . For this reason the threshold element assumes an important role in pattern - classifying machines . We shall use the block diagram of Fig . 1.5 as a basic model of a two - category ...
Sivu 21
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a -1 signal if g ( X ) < 0. We must then associate a TLU output of ...
... element , is called a threshold logic unit ( TLU ) . We shall ordinarily assume that the threshold element is a device which responds with a +1 signal if g ( X ) > 0 and a -1 signal if g ( X ) < 0. We must then associate a TLU output of ...
Sivu 128
... elements of A are positive , the next p2 diago- nal elements are negative , and the last d ( P1 + p2 ) diagonal elements are zero , where d is the order of A , and ( p1 + p2 ) is the rank of A. The diagonal elements of A are the ...
... elements of A are positive , the next p2 diago- nal elements are negative , and the last d ( P1 + p2 ) diagonal elements are zero , where d is the order of A , and ( p1 + p2 ) is the rank of A. The diagonal elements of A are the ...
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 |