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 ...
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 ...
Sivu 111
For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H. be a matrix Hy whose rows consist of those U vectors for which H ( U ) +1 . Let Hy be a matrix whose rows consist of the remaining U vectors .
For some of these U vectors , H ( U ) = 1 , and for the remaining , H ( U ) = -1 . Let H. be a matrix Hy whose rows consist of those U vectors for which H ( U ) +1 . Let Hy 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 discriminant functions . Thus a PWL machine consists of R banks of subsidiary ...
A PWL machine consists of R discriminators , where R is the number of pattern categories . Each discriminator employs a number of subsidiary linear discriminant functions . Thus a PWL machine consists of R banks of subsidiary ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |