Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 99
6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y. and Y2 , comprising the training set y , and we wish to find a committee machine of size P to separate these subsets .
6.3 A training procedure for committee machines Suppose that we have training pattern subsets Y. and Y2 , comprising the training set y , and we wish to find a committee machine of size P to separate these subsets .
Sivu 119
It should be observed that if the two density functions 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 functions 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 ...
Sivu 121
if N is the total number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most ...
if N is the total number of patterns in the training subsets . The value of k / N , however , should decrease toward zero with increasing N. The high storage requirements of the Fix and Hodges method render it impractical in most ...
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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 |