Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 20
... linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , exist such that gi ( X ) > gi ( X ) j = 1 , · • • 9 XR are · , 9R R , ji for all X in X ; for all i ( 2.9 ) = 1 , R • 9 As a ...
... linear and the subsets X1 , X2 , linearly separable if and only if linear discriminant functions g1 , 92 , exist such that gi ( X ) > gi ( X ) j = 1 , · • • 9 XR are · , 9R R , ji for all X in X ; for all i ( 2.9 ) = 1 , R • 9 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 ...
... 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 ...
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 91 ( ) and ( 1 ) are linearly separable ...
... 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 91 ( ) and ( 1 ) are linearly separable ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |