Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Therefore a TLU can be used as the optimum classifying machine . * The block diagram of the TLU is given in Fig . 3.1 . The weight * There is an obvious generalization of this example for the case R > 2. The reader is invited to verify ...
Therefore a TLU can be used as the optimum classifying machine . * The block diagram of the TLU is given in Fig . 3.1 . The weight * There is an obvious generalization of this example for the case R > 2. The reader is invited to verify ...
Sivu 55
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for normal patterns ...
A set of normal patterns would then tend to be grouped in an ellipsoidal cluster centered around a prototype pattern M. 3.8 The optimum classifier for normal patterns We are now ready to derive the optimum classifier for normal patterns ...
Sivu 119
It should be observed that if the two density functions overlap sufficiently , it is likely that this optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume ...
It should be observed that if the two density functions overlap sufficiently , it is likely that this optimum decision surface will not perfectly separate all the members of the two training subsets . If we were willing to assume ...
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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 |