Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also involves shuffling the ...
must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also involves shuffling the ...
Sivu 118
Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been ... Such would be the case if the jth bank , j * i , contained the largest subsidiary discriminant .
Training patterns are presented to the PWL machine whose R banks of subsidiary linear discriminant functions have initially been ... Such would be the case if the jth bank , j * i , contained the largest subsidiary discriminant .
Sivu 123
Which of the weight vectors belonging to the ith bank is the closest to Xk + 1 can now be determined , using the PWL machine ... vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
Which of the weight vectors belonging to the ith bank is the closest to Xk + 1 can now be determined , using the PWL machine ... vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
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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 |