Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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For one of them , called the fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern ...
For one of them , called the fixed - increment rule , c is taken to be any fixed number greater than zero . When c is equal to one , for example , each weight is altered by the addition ( or subtraction ) of the corresponding pattern ...
Sivu 77
The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time . Ridgway 10 later suggested a modification of the Widrow ...
The fixed - increment and absolute correction rules were first proposed by Rosenblatt , 13 although Widrow and Hoff introduced a similar rule at substantially the same time . Ridgway 10 later suggested a modification of the Widrow ...
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INDEX Abramson , 61 , 62 , 63 Absolute correction rule , 70 , 81 ADALINES , 77 Adaptive decision networks , 2 Adaptive sample set construction , 125 Adjusted training set , 81 Adjustment , of discriminant functions , 8 of weight vectors ...
INDEX Abramson , 61 , 62 , 63 Absolute correction rule , 70 , 81 ADALINES , 77 Adaptive decision networks , 2 Adaptive sample set construction , 125 Adjusted training set , 81 Adjustment , of discriminant functions , 8 of weight vectors ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |