Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 70
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 71
In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error .
In one case , c is a fixed constant so that the distance moved toward a particular pattern hyperplane is always the same . This fixed distance may or may not be sufficient to cross the pattern hyperplane and thus correct the error .
Sivu 107
Committee machines always have a fixed hyperplane in the Il space , and this hyperplane is the perpendicular bisector of a major diagonal of the image - space cube . The process of training the committee machine is then a search for a ...
Committee machines always have a fixed hyperplane in the Il space , and this hyperplane is the perpendicular bisector of a major diagonal of the image - space cube . The process of training the committee machine is then a search for a ...
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Sisältö
I | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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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 |