Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson ... 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 ... 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 . Of course , the location and ... Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis- criminators ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. discriminant functions . Of course , the location and ... Our discriminant- function pattern classifier , illustrated in Fig . 1.4 , would employ R dis- criminators ...
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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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ 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 |