Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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An example of a deliberate omission is the subject of measurement selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in ...
An example of a deliberate omission is the subject of measurement selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in ...
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1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical ...
1.3 The problem of what to measure In assuming that the data to be classified consist of d real numbers , we are obliged to mention , at least briefly , the difficulties that attend selecting these numbers from any given physical ...
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In the following sections we shall discuss a class of methods by which this selection might be made . · 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways .
In the following sections we shall discuss a class of methods by which this selection might be made . · 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways .
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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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 |