Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 7
Sivu 58
... sample covariance matrix . The first step in its de- velopment is to form a matrix Q : whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N ; ...
... sample covariance matrix . The first step in its de- velopment is to form a matrix Q : whose columns are derived from the patterns in X. Subtract from each of the N ; patterns in X ; the sample- mean pattern ( X ) ;; Q ; is then a d X N ; ...
Sivu 62
... sample covariance matrix will be singular as was pointed out in Sec . 3 · 10 . Kanal and Randall discuss this problem and recommend a remedy originally proposed by Harley.1 10 The material in Sec . 3.11 on learning the mean vector of ...
... sample covariance matrix will be singular as was pointed out in Sec . 3 · 10 . Kanal and Randall discuss this problem and recommend a remedy originally proposed by Harley.1 10 The material in Sec . 3.11 on learning the mean vector of ...
Sivu 136
... sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
... sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ 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 |