Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 101
... dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the pattern vector to ( or from ) the ...
... dot products closest to zero . ( Ties are resolved arbitrarily . ) These , in one sense , are the easiest to adjust . The adjustment is achieved by the familiar process of adding ( or subtracting ) the pattern vector to ( or from ) the ...
Sivu 102
... dot products with each of the pattern vectors Y1 , Y2 , Ys ; then , adjustments to the weight vector ( s ) are made whenever Nx < 0. ( The reader could assume , for example , that Y1 contains Y2 and that Y2 contains - Y1 and -Y3 . ) k 1 ...
... dot products with each of the pattern vectors Y1 , Y2 , Ys ; then , adjustments to the weight vector ( s ) are made whenever Nx < 0. ( The reader could assume , for example , that Y1 contains Y2 and that Y2 contains - Y1 and -Y3 . ) k 1 ...
Sivu 103
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
... dot products with Y1 ) . At the next stage , examining the weight - vector positions with respect to the Y2 pattern hyperplane we see that all of them ( hence , again , the majority ) are on the correct side ; thus , no adjustments are ...
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 |