Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 35
Sivu 32
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of ... number of linear dichotomies of N points dimensions, 1.
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of ... number of linear dichotomies of N points dimensions, 1.
Sivu 41
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover . " Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
... number of linear dichotomies and the extension of these results to surfaces are also due to Cover . " Based on experimental and theoretical results on the number of linear dichotomies , both Koford12 and Brown13 suggested that the ...
Sivu 67
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only if there is a region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count the ...
... linear dichotomy of the N patterns , and , conversely , a dichotomy of the patterns in y is a linear dichotomy only if there is a region in weight space corresponding to it . * For any given linear dichotomy , the corre- * If we count 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 |