Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 6
Sivu 80
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine given ...
... Sy , is any infinite sequence of patterns such that Sy = Y1 , Y 2 , Yk , • 1. Each Y in Sy is a member of y . 2. Every element of y occurs infinitely often in Sy . ( 5.2 ) The training problem for a two - category linear machine given ...
Sivu 88
... Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ2 , SŴR gener- ated from Sy by the above rule will be called ...
... Sy all pattern vectors for which ( a ) occurs . The resulting sequence Sy will be called the reduced training se- quence . The resulting weight - vector sequences Sŵ ,, Sŵ2 , SŴR gener- ated from Sy by the above rule will be called ...
Sivu 90
... sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth mem- bers of Sŵ ,, . .. , Sŵ are given by W , ( * ) , ŴR ...
... sequence Sy of RD - dimensional weight vectors from the reduced weight - vector sequences , Sŵ ,, . . . , SŴR . Let V be the kth member of the sequence Sv . If the respective kth mem- bers of Sŵ ,, . .. , Sŵ are given by W , ( * ) , ŴR ...
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 |