Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 89
... reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
... reduced training sequence Sŷ and the reduced weight - vector sequences Sŵ1 , SWR a corresponding sequence of vectors from the set Z. Let us denote this sequence of vectors from Z by the symbol Sz . Corresponding to the kth member , Ŷ of ...
Sivu 90
... 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 ( * ) then V is deter- mined as follows : The first ...
... 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 ( * ) then V is deter- mined as follows : The first ...
Sivu 136
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
... training theorem , 87 rth - order polynomial functions , 30 , 38 Randall , 62 , 63 Rank of sample covariance matrix , 58 Rao , 12 , 13 Reduced training sequence , 82 Reduced weight - vector sequence , 82 Reduction of number 136 INDEX 65.
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 |