Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 20
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
Sivu 23
... Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is ...
... Note from Fig . 2.5 that the absolute value of n⚫ P is the normal Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / w ) . ( If A > 0 , the origin is ...
Sivu 39
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) ...
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2+ ) ( M + 1 ) ...
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 |