Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
discriminant functions . Of course , the location and form of the decision surfaces do not uniquely specify the discriminant functions . For one thing , the same arbitrary constant can be added to each discriminant function without ...
Sivu 16
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
A particular function belonging to this family can be selected by choosing the appropriate values of the parameters . The training of a machine restricted to employ discriminant functions belonging to a particular family can then be ...
Sivu 112
Examination of Eq . ( 6.13 ) reveals that each subsidiary discriminant function is a linear combination of the functions fı ... A layered machine with P TLUs in the first layer has a total of 2P linear subsidiary discriminant functions ...
Examination of Eq . ( 6.13 ) reveals that each subsidiary discriminant function is a linear combination of the functions fı ... A layered machine with P TLUs in the first layer has a total of 2P linear subsidiary discriminant functions ...
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adjusted apply assume bank belonging to category called changes Chapter classifier cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed gi(X given illustrated implemented important initial known layered machine linear dichotomies linear machine linearly separable negative 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 reduced regions respect response rule sample mean selected separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns 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 |