Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 20
... 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 • · 9 gi ( X ) > gi ( X ) = j 1 ...
... 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 • · 9 gi ( X ) > gi ( X ) = j 1 ...
Sivu 21
... separable , then each pair of subsets Xi , X ,, i , j = 1 , . R , ij , is also linearly sepa- • 9 • " rable . That is , if X1 , X2 , XR are linearly separable , then X1 , X2 , XR are also pairwise linearly separable . 2.6 The threshold ...
... separable , then each pair of subsets Xi , X ,, i , j = 1 , . R , ij , is also linearly sepa- • 9 • " rable . That is , if X1 , X2 , XR are linearly separable , then X1 , X2 , XR are also pairwise linearly separable . 2.6 The threshold ...
Sivu 107
... separable , and thus a two - layer machine suffices to perform the pattern dichotomization . 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient condition it will be helpful to make ...
... separable , and thus a two - layer machine suffices to perform the pattern dichotomization . 6.6 A sufficient condition for image - space linear separability Before stating and proving the sufficient condition it will be helpful to make ...
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 gi(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 Stanford subsets X1 subsidiary discriminant functions Suppose terns TLU response 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 |