Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 62
... derivation is given by Minsky . Winder 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 another ...
... derivation is given by Minsky . Winder 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 another ...
Sivu 77
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4-3 stem from a variety of sources . The fixed - increment and absolute ...
... derivation of L ( N , d ) given in the footnote on page 67 follows the derivation by Cameron , 12 The error - correction training procedures discussed in Sec . 4-3 stem from a variety of sources . The fixed - increment and absolute ...
Sivu 109
... Derivation of a discriminant function for a layered machine It was mentioned in Sec . 6.1 that the discriminant functions of layered machines are piecewise linear . In this section , we shall verify this statement . Consider the first ...
... Derivation of a discriminant function for a layered machine It was mentioned in Sec . 6.1 that the discriminant functions of layered machines are piecewise linear . In this section , we shall verify this statement . Consider the first ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |