Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
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... separable . Stated another way , a classification of X is linear and the subsets X1 , X2 , XR are linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that ... gi ( X ) > g ; ( X ) j = 1 , . . R ...
... separable . Stated another way , a classification of X is linear and the subsets X1 , X2 , XR are linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that ... gi ( X ) > g ; ( X ) j = 1 , . . R ...
Sivu 21
... separable , then X1 , X2 , XR are also pairwise linearly separable . 2.6 The threshold logic unit ( TLU ) If R = 2 , a linear machine employs a single linear discriminant function g ( X ) defined by g ( X ) = W1X1 + W2X2 + • + waxa + wa ...
... separable , then X1 , X2 , XR are also pairwise linearly separable . 2.6 The threshold logic unit ( TLU ) If R = 2 , a linear machine employs a single linear discriminant function g ( X ) defined by g ( X ) = W1X1 + W2X2 + • + waxa + wa ...
Sivu 107
... separable , and thus a two - layer machine suffices to perform the pattern dichotomization . 1 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient condition it will be helpful to ...
... separable , and thus a two - layer machine suffices to perform the pattern dichotomization . 1 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient condition it will be helpful to ...
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 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 wa+1 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 |