Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 70
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
... fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern component . This adjustment may ...
Sivu 71
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
... fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance . may or may not be sufficient to cross the pattern hyperplane and thus correct the error . In another case , c is ...
Sivu 107
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 2 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
... fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves 91 ( 1 ) and 2 ( 1 ) linearly separable . For any given training subsets X1 and X2 it would be of ...
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 |