Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 85
5.4 Proof 2 The following proof of Theorem 5.1 results from a simple geometric argument revealing that it is impossible to apply the fixed - increment error - correction procedure and remain forever outside the region of solu- tion ...
5.4 Proof 2 The following proof of Theorem 5.1 results from a simple geometric argument revealing that it is impossible to apply the fixed - increment error - correction procedure and remain forever outside the region of solu- tion ...
Sivu 89
Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying Theorem 5.1 . The first step is to generate a new set Z of higher ...
Proof The proof of Theorem 5.2 is accomplished by reformulating the R - category problem as a dichotomy problem in a higher - dimensional space and then applying Theorem 5.1 . The first step is to generate a new set Z of higher ...
Sivu 108
Proof - We have P TLUs , each of which implements a hyperplane in the pattern space . In this proof it will be convenient for the TLUS to have 0 , 1 responses rather than 1 , 1 responses . Since exactly P + 1 cells are occupied by ...
Proof - We have P TLUs , each of which implements a hyperplane in the pattern space . In this proof it will be convenient for the TLUS to have 0 , 1 responses rather than 1 , 1 responses . Since exactly P + 1 cells are occupied by ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |