Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 98
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUs to have ...
... consists of the vote - taking TLU whose response is the majority response of the committee TLUs . A committee machine of size P is depicted in Fig . 6.4 . The committee machine can be generalized by allowing the com- mittee TLUs to have ...
Sivu 111
... consist of those U vectors for which H ( U ) +1 . Let H2 be a matrix whose rows consist of the remaining U vectors . If there are L U vec- tors for which H ( U ) = +1 , then H1 will be an L X P matrix and H2 will be a ( 2P L ) X P ...
... consist of those U vectors for which H ( U ) +1 . Let H2 be a matrix whose rows consist of the remaining U vectors . If there are L U vec- tors for which H ( U ) = +1 , then H1 will be an L X P matrix and H2 will be a ( 2P L ) X P ...
Sivu 115
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
... consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear dis- criminant functions . Thus a PWL machine consists of R banks of sub- sidiary discriminators ...
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 |