Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... modes , 121 7.6 Mode - seeking and related training methods for PWL machines , 122 7.7 Bibliographical and historical remarks , 125 References , 126 APPENDIX A.1 Separation of a quadratic form into positive and negative parts , 127 A.2 ...
... modes , 121 7.6 Mode - seeking and related training methods for PWL machines , 122 7.7 Bibliographical and historical remarks , 125 References , 126 APPENDIX A.1 Separation of a quadratic form into positive and negative parts , 127 A.2 ...
Sivu 121
... modes , given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. for i = 1 , . . . , R and j = 1 , . . . , L. That is , there are L1 typical patterns ...
... modes , given the training subsets . Suppose the modes for the various categories , as established by a training procedure , are given by the points P. for i = 1 , . . . , R and j = 1 , . . . , L. That is , there are L1 typical patterns ...
Sivu 122
... mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to those made by the Fix and Hodges method . What is needed to apply the closest - mode method is a means of training a PWL machine such that the ...
... mode . Thus the " closest - mode " method just de- scribed will often make decisions identical to those made by the Fix and Hodges method . What is needed to apply the closest - mode method is a means of training a PWL machine such that the ...
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 |