Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 117
must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the ...
must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the ...
Sivu 118
Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been se- ... Such would be the case if the jth bank , ji , contained the largest subsidiary discriminant .
Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been se- ... Such would be the case if the jth bank , ji , contained the largest subsidiary discriminant .
Sivu 122
L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category ...
L. Thus there are R banks of weight vectors , and the ith bank has Li members . Any training method which adjusts the weight vectors in each bank so that each weight vector finally resides in the center of a cluster of like - category ...
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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 |