Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 48
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 ...
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 ...
Sivu 119
It should be observed that if the two density func- tions 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 func- tions 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
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 |