Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu xi
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
... Transformation properties of layered machines , 103 6.6 A sufficient condition for image - space linear separability , 107 6.7 Derivation of a discriminant function for a layered machine , 109 6.8 Bibliographical and historical remarks ...
Sivu 103
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
... Transformation properties of layered machines We have seen in Secs . 6-2 to 6-4 that the concept of the first - layer TLUS as voters in a " committee " is a productive representation for two - layer machines . Another representation ...
Sivu 107
... transformation such that g1 ( 1 ) and ( 2 ) are placed on opposite sides of the fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves ( 1 ) and 2 ( 1 ) ...
... transformation such that g1 ( 1 ) and ( 2 ) are placed on opposite sides of the fixed image - space hyperplane . If the image - space hyperplane is not fixed , then we need only find a transformation which leaves ( 1 ) and 2 ( 1 ) ...
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 function g(X 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 shown in Fig solution weight vectors 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 |