Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 34
Sivu 52
... equal probability density ( 21220122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 V1 +012 When σ12 is zero , the ...
... equal probability density ( 21220122122 + 222 = constant ) are ellipses , cen- tered on the origin , whose major axes lie along the line 21 = 22. The eccentricities of the ellipses are equal to 2012 V1 +012 When σ12 is zero , the ...
Sivu 58
... equal to rank QQ , which is equal to rank Qi , and since rank Q : min ( d , N1 ) , rank ( E ) ; < d if N ; < d . If N ; d , Q ; will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
... equal to rank QQ , which is equal to rank Qi , and since rank Q : min ( d , N1 ) , rank ( E ) ; < d if N ; < d . If N ; d , Q ; will have rank equal to d if and only if there are no linear dependencies among the rows of Q. Or ...
Sivu 90
... equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
... equal to -Ŷ , and whose other components are all equal to zero . We apply this rule to each element of Sy to generate the sequence Sz . The final step of the proof is to form a sequence Sy of RD - dimensional weight vectors from the ...
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 |