Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 41
Sivu 47
... assume that R = 2 ; that is , there are two categories , labeled category 1 and category 2 . We shall carry out the steps involved in the specification of the dis- criminant functions for the optimum classifying machine to illustrate ...
... assume that R = 2 ; that is , there are two categories , labeled category 1 and category 2 . We shall carry out the steps involved in the specification of the dis- criminant functions for the optimum classifying machine to illustrate ...
Sivu 49
... assume that some or all of the values of the Pi , the qi , and p ( 1 ) are unknown , the next step in the parametric training procedure consists in examining typical patterns to make estimates for the unknown values of the pi , qi , and ...
... assume that some or all of the values of the Pi , the qi , and p ( 1 ) are unknown , the next step in the parametric training procedure consists in examining typical patterns to make estimates for the unknown values of the pi , qi , and ...
Sivu 122
... assume that the k closest training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make ...
... assume that the k closest training patterns to a given pattern X will often include a predominant number of patterns from the cluster sur- rounding the closest mode . Thus the " closest - mode " method just de- scribed will often make ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix d-dimensional decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X 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 |