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 ...
Sivu 133
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
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding covariance decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear machine linearly separable matrix measurements negative normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side solution space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |