Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
Sivu 33
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
... derivations to follow in this and subsequent sections , we shall use some facts from geometry which , while obvious for two- and three - dimensional spaces , happen to be valid in any finite - dimensional space . Of course , each of ...
Sivu 41
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. derivation of this number given in Sec . 2.13 is a version of one given by Cover . 7,8 The effects of constraints on the number of linear dichotomies and the extension ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. derivation of this number given in Sec . 2.13 is a version of one given by Cover . 7,8 The effects of constraints on the number of linear dichotomies and the extension ...
Sivu 62
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 ∙ 14 ) and ( 3 · 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests ...
... derivation is given by Minsky . Winder1 has determined that the weights specified by Eqs . ( 3 ∙ 14 ) and ( 3 · 15 ) of this example will realize only a small percentage of the linearly separable switching functions and suggests ...
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 |