Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 97
6.2 Committee machines Suppose we have training subsets Y1 and Y2 of augmented training pat- terms which are not linearly separable . That is , no vector W exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) ...
6.2 Committee machines Suppose we have training subsets Y1 and Y2 of augmented training pat- terms which are not linearly separable . That is , no vector W exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) ...
Sivu 98
But consider the " committee " of weight vectors W1 , W2 , and W3 in Fig . 6.3 . With respect to these weight vectors , we have the inequalities W1 1 W1 . Y1 > 0 W1 · Y2 > 0 • 2 W1 Y3 > 0 W2 . Y2 > 0 2 W2 . Y3 < 0 ( 6.4 ) W3 .
But consider the " committee " of weight vectors W1 , W2 , and W3 in Fig . 6.3 . With respect to these weight vectors , we have the inequalities W1 1 W1 . Y1 > 0 W1 · Y2 > 0 • 2 W1 Y3 > 0 W2 . Y2 > 0 2 W2 . Y3 < 0 ( 6.4 ) W3 .
Sivu 99
Therefore , our discussion will concentrate on the " simple - majority " committee machine with a fixed vote - taking TLU . * 1 x X Response Vote - taking TLU ( second layer ) Pattern = +1 d + 1 P committee TLUS ( first layer ) FIGURE ...
Therefore , our discussion will concentrate on the " simple - majority " committee machine with a fixed vote - taking TLU . * 1 x X Response Vote - taking TLU ( second layer ) Pattern = +1 d + 1 P committee TLUS ( first layer ) FIGURE ...
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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 |