Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 14
Sivu 95
... binary responses of some TLUS are used as inputs to other TLUS . If R = 2 , the binary output of one of the TLUS is taken to be the response of the whole machine . Figure 6.1 is an example of such a network . The properties of TLU ...
... binary responses of some TLUS are used as inputs to other TLUS . If R = 2 , the binary output of one of the TLUS is taken to be the response of the whole machine . Figure 6.1 is an example of such a network . The properties of TLU ...
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 |
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 |