Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 37
Sivu 32
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
... number of dichotomies of N patterns that its members could effect . We shall show that if the positions of the N pattern points satisfy some quite mild conditions , the number of dichotomies that can be implemented by a function will ...
Sivu 120
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
... patterns in the training subsets . Many of these nonparametric rules actually lead to the same discriminant ... number of patterns in each of the training subsets . Next we select some metric with which to measure distance in the pattern ...
Sivu 121
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
... number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most pattern - classification ...
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 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 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 second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |