Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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4 • 5 An error - correction training procedure for R > 2 > a A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
4 • 5 An error - correction training procedure for R > 2 > a A linear machine for classifying patterns belonging to more than two categories was defined in Chapter 2. It consists of R linear discriminators and a maximum selector ( Fig .
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Let Sw be the weight - vector sequence generated by any training sequence Sy , using the fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , .
Let Sw be the weight - vector sequence generated by any training sequence Sy , using the fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , .
Sivu 119
Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the ...
Suppose that we decided to use an error - correction training procedure to train a single TLU . Even though a TLU is capable of implementing the optimum decision surface , an error - correction procedure could never stabilize at the ...
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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 |