Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 23
Sivu 20
... 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 ... exists which has each member of X1 on one side and each member of X2 on the other side . Because the decision ...
... 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 ... exists which has each member of X1 on one side and each member of X2 on the other side . Because the decision ...
Sivu 84
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
Sivu 97
... exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . In the next three ... exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) Therefore , it would be impossible ...
... exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . In the next three ... exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) Therefore , it would be impossible ...
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 |