Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 20
... linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that · • 9 XR are gi ( X ) > g ; ( X ) j = 1 ,. , R , ji for all X in Xi for all i = 1 , ( 2.9 ) R " = As a ...
... linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that · • 9 XR are gi ( X ) > g ; ( X ) j = 1 ,. , R , ji for all X in Xi for all i = 1 , ( 2.9 ) R " = As a ...
Sivu 21
... linearly separable , then each pair of subsets Xi , X ,, i , j = 1 , , R , ij , is also linearly sepa- rable . That is , if X1 , X2 , XR are linearly separable , then X1 , X2 , . • • , XR are also pairwise linearly separable . 2.6 The ...
... linearly separable , then each pair of subsets Xi , X ,, i , j = 1 , , R , ij , is also linearly sepa- rable . That is , if X1 , X2 , XR are linearly separable , then X1 , X2 , . • • , XR are also pairwise linearly separable . 2.6 The ...
Sivu 107
... linearly separable . For any given training subsets X1 and X2 it would be of interest to know neces- sary and sufficient conditions on the hyperplanes implemented by the first - layer TLUs such that g1 ( 1 ) and 2 ( 1 ) are linearly ...
... linearly separable . For any given training subsets X1 and X2 it would be of interest to know neces- sary and sufficient conditions on the hyperplanes implemented by the first - layer TLUs such that g1 ( 1 ) and 2 ( 1 ) are linearly ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |