Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 83
... initial weight vector Ŵ1 . j Since for each Ŷ ; in Sy and Ŵ ; in Sŵ , Ŷ ; • Ŵ ; ≤ 0 , we have from Eq . ( 5.8 ) = k + 1 1 Ŵ1 + Ŷ1 + Ŷ1⁄2 + · 2 + Ŷk ( 5.9 ) We shall prove the theorem for the case Ŵ1 = O , although essentially the same ...
... initial weight vector Ŵ1 . j Since for each Ŷ ; in Sy and Ŵ ; in Sŵ , Ŷ ; • Ŵ ; ≤ 0 , we have from Eq . ( 5.8 ) = k + 1 1 Ŵ1 + Ŷ1 + Ŷ1⁄2 + · 2 + Ŷk ( 5.9 ) We shall prove the theorem for the case Ŵ1 = O , although essentially the same ...
Sivu 88
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) . Yk j = 1 , ... , R , ji or ( b ) there exists some l = 1 , W ( ) . YkW , ( k ) • Yk R , li for which j = 1 ...
... initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) . Yk j = 1 , ... , R , ji or ( b ) there exists some l = 1 , W ( ) . YkW , ( k ) • Yk R , li for which j = 1 ...
Sivu 103
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
... initial region . * This same phenomenon accounts for instances in which the committee train- ing procedure does not converge even when the initial weight vectors are all of the same length . Occasionally one of the weight vectors ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |