Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 103
... binary outputs of the first layer of TLUS can be regarded as the components of a vector . If there are P1TLUS in the first layer , these TLUS transform the d - dimen- sional input pattern vector into a Pi - dimensional vector with binary ...
... binary outputs of the first layer of TLUS can be regarded as the components of a vector . If there are P1TLUS in the first layer , these TLUS transform the d - dimen- sional input pattern vector into a Pi - dimensional vector with binary ...
Sivu 111
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
... binary vector with P components there are 2o distinct U vectors . For some of these U vec- tors , H ( U ) = 1 , and for the remaining , H ( U ) = −1 . Let H1 be a matrix whose rows consist of those U vectors for which H ( U ) +1 . Let ...
Sivu 136
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
... binary pat- terns , 48 for normal patterns , 55 Optimum machines , 44 Overlapping probability distributions , 118 Overrelaxation , 77 Pairwise linearly separable subsets , 21 Parameters , 9 , 15 , 43 , 44 Parametric training , 9 , 10 ...
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 |