Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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The second layer 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- ...
The second layer 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- ...
Sivu 111
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 H2 be a matrix whose rows consist of the remaining 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 H2 be a matrix whose rows consist of the remaining U vectors .
Sivu 115
A PWL machine 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 ...
A PWL machine 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 ...
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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 |
Kirjaluettelon tiedot
| Otsikko | Learning Machines: Foundations of Trainable Pattern-classifying Systems McGraw-Hill series in systems science |
| Kirjoittaja | Nils J. Nilsson |
| Painos | kuvitettu |
| Kustantaja | McGraw-Hill, 1965 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |