Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 88
2 The rule for generating these sequences is as follows : W1 ( 1 ) , W2 ( 1 ) , WR ) are arbitrary initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . or Ꭱ Then , either ( a ) W1 ( k ) .
2 The rule for generating these sequences is as follows : W1 ( 1 ) , W2 ( 1 ) , WR ) are arbitrary initial weight vectors ; Y belongs to one of the train- ing subsets , say Yi . or Ꭱ Then , either ( a ) W1 ( k ) .
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Wk < 0 • Wk + 1 = Wk • if Yk ' · Wk > 0 ( 5.35 ) We note that the value of the correction increment is given by Ck = λ WxYk ' Yk ' • Yk ' ( 5.36 ) • After specifying an initial weight vector W1 we may remove from the training sequence ...
Wk < 0 • Wk + 1 = Wk • if Yk ' · Wk > 0 ( 5.35 ) We note that the value of the correction increment is given by Ck = λ WxYk ' Yk ' • Yk ' ( 5.36 ) • After specifying an initial weight vector W1 we may remove from the training sequence ...
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1 3 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 W2 ( 1 ) were many times longer ( in the same direction ) than is shown in Fig .
1 3 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 W2 ( 1 ) were many times longer ( in the same direction ) than is shown in Fig .
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |