Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 5
Sivu 104
... image space or the I1 space . The trans- formation between the pattern space and the I1 space depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of ...
... image space or the I1 space . The trans- formation between the pattern space and the I1 space depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of ...
Sivu 105
... image - space cube in accordance with the TLU 5 7 TLU 3 8 6 TLU 1 TLU 2 Origin * 1 ( a ) Pattern space 1,4,5,8 3 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 ...
... image - space cube in accordance with the TLU 5 7 TLU 3 8 6 TLU 1 TLU 2 Origin * 1 ( a ) Pattern space 1,4,5,8 3 TLU 3 Origin TLU 2 * 3,7 TLU 1 ( b ) Image space 2 FIGURE 6.6 Pattern - space to image - space transformation numbers 1 , 2 ...
Sivu 108
... image - space verti- ces . Let us form a P × ( P + 1 ) matrix M whose columns are the image- space vertices ; M is a binary matrix with elements equal to 0 and 1 . Because the partition is nonredundant , if we remove any row from M , at ...
... image - space verti- ces . Let us form a P × ( P + 1 ) matrix M whose columns are the image- space vertices ; M is a binary matrix with elements equal to 0 and 1 . Because the partition is nonredundant , if we remove any row from M , at ...
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 |