Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson ... 2 3 Rain tomorrow No rain tomorrow Undecided We shall adopt as our basic model of a pattern classifier a device with d input lines and one output line ( see Fig .
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson ... 2 3 Rain tomorrow No rain tomorrow Undecided We shall adopt as our basic model of a pattern classifier a device with d input lines and one output line ( see Fig .
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. discriminant functions . ... Our discriminantfunction pattern classifier , illustrated in Fig . 1.4 , would employ R discriminators , each of which computes the value ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. discriminant functions . ... Our discriminantfunction pattern classifier , illustrated in Fig . 1.4 , would employ R discriminators , each of which computes the value ...
Sivu 9
The performance level which the pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training . The training process proceeds as follows : a large number of patterns ...
The performance level which the pattern classifier is eventually to achieve must be achieved largely by an adjustment process , which has become known as training . The training process proceeds as follows : a large number of patterns ...
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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 |