Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 83
Let S be the reduced weight - vector sequence resulting from the application of the fixed - increment error - correction rule begin- ning with initial weight vector Ŵ1 . j Since for each Ŷ ; in Sy and Ŵ ; in Sŵ , Ŷ ; • Ŵ ; ≤ 0 , we ...
Let S be the reduced weight - vector sequence resulting from the application of the fixed - increment error - correction rule begin- ning with initial weight vector Ŵ1 . j Since for each Ŷ ; in Sy and Ŵ ; in Sŵ , Ŷ ; • Ŵ ; ≤ 0 , we ...
Sivu 88
The rule for generating these sequences is as follows : W1 ) , W2 ( 1 ) , 2 WR ) are arbitrary initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) .
The rule for generating these sequences is as follows : W1 ) , W2 ( 1 ) , 2 WR ) are arbitrary initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . Then , either ( a ) W ( k ) . Yk > W ( ) .
Sivu 103
1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W21 ) were many times longer ( in the same direction ) than is shown in Fig .
1 This example can also be used to illustrate the necessity for begin- ning with initial weight vectors of approximately the same length . Sup- pose that W21 ) were many times longer ( in the same direction ) than is shown in Fig .
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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 |