Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 89
... reduced training sequence Sy and the reduced weight - vector sequences Sŵ ,, SŴR 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 Sy and the reduced weight - vector sequences Sŵ ,, SŴR 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 Sy . If the respective kth mem- bers of Sŵ1 , . , Sŵ are given by Ŵ1 ( * ) , ŴR ) then V is deter- mined as follows : The first block of D ...
... reduced weight - vector sequences , Sŵ ,, ... , SŴR . Let V be the kth member of the sequence Sy . If the respective kth mem- bers of Sŵ1 , . , Sŵ are given by Ŵ1 ( * ) , ŴR ) then V is deter- mined as follows : The first block of D ...
Sivu 91
... reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced weight ...
... reduced training sequence Sy then creates a reduced weight - vector sequence Sŵ such that · k k ( 5.37 ) for all Ŷ in Sŷ and for all Ŵ in Sŵ . Theorem 5.3 k Let y ' be a set of linearly contained patterns . Let S✩ be the reduced weight ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |