Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 121
... Fix and Hodges method render it impractical in most pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to ...
... Fix and Hodges method render it impractical in most pattern - classification tasks . To classify any pat- tern X , the distance between X and each of the patterns in the training subsets must be computed . If these computations are to ...
Sivu 122
... Fix and Hodges method . The concept of distance still plays an important role in a way which preserves some of the features of the Fix and Hodges method . It seems reasonable to assume that the k closest training patterns to a given ...
... Fix and Hodges method . The concept of distance still plays an important role in a way which preserves some of the features of the Fix and Hodges method . It seems reasonable to assume that the k closest training patterns to a given ...
Sivu 134
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density ...
... Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian probability - density ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges gi(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 parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying 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 |