Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 63
Sivu 9
Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the training set . We shall say that these discriminant functions are obtained by training .
Discriminant functions are then chosen , by methods to be discussed in general below and more specifically later , which perform adequately on the training set . We shall say that these discriminant functions are obtained by training .
Sivu 11
Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods .
Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these training meth- ods seems to be more extensive than the theory supporting the nonpara- metric training methods .
Sivu 122
In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the modes or ...
In the next section we shall present a candidate training method . 7.6 Mode - seeking and related training methods for PWL machines To apply the closest - mode decision method , we need a training procedure to locate the modes or ...
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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 |