Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 20
... 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 ... 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 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 ... 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 118
... exists , have not yet been determined . 7.3 A disadvantage of the error - correction training methods Throughout ... exist probability distributions for each category , although we are unwilling to make any assumption about the forms 118 ...
... exists , have not yet been determined . 7.3 A disadvantage of the error - correction training methods Throughout ... exist probability distributions for each category , although we are unwilling to make any assumption about the forms 118 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |