Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 81
Theorem 5.1 Let the training subsets Yi and Yz be linearly separable . Let Sw be the weight - vector sequence generated by any training sequence Sy , using the fixed - increment error - correction procedure and beginning with any ...
Theorem 5.1 Let the training subsets Yi and Yz be linearly separable . Let Sw be the weight - vector sequence generated by any training sequence Sy , using the fixed - increment error - correction procedure and beginning with any ...
Sivu 88
ܙ1 Theorem 5.2 Let the training subsets Yı , Y2 , . . . , Yr be linearly separable . Let Y1 42 Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by any training sequence Sy , using the generalized fixed - increment ...
ܙ1 Theorem 5.2 Let the training subsets Yı , Y2 , . . . , Yr be linearly separable . Let Y1 42 Sw , Sw ,, . . . , SwR be the weight - vector sequences generated by any training sequence Sy , using the generalized fixed - increment ...
Sivu 93
Block has proved a generalized version of Theorem 5.1 in which the correction increment Ck of Eq . ( 5.4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
Block has proved a generalized version of Theorem 5.1 in which the correction increment Ck of Eq . ( 5.4 ) need not be independent of k . Theorem 5.2 was first proved by C. Kesler at Cornell University .
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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 Development 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 networks 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 regions respect response rule sample mean selection separable shown side solution space Stanford step Suppose theorem theory threshold training methods 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 |