Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 12
Sivu 34
... the same partition of X ' FIGURE 2.10 Two separations of X in E2 = - in X ' . We now have a set of N 1 lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on ...
... the same partition of X ' FIGURE 2.10 Two separations of X in E2 = - in X ' . We now have a set of N 1 lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on ...
Sivu 107
We know for example that it is necessary that these hyperplanes partition X1 and X2 . A set of conditions that are both necessary and sufficient have not yet been found , but in the next section we shall state some sufficient conditions ...
We know for example that it is necessary that these hyperplanes partition X1 and X2 . A set of conditions that are both necessary and sufficient have not yet been found , but in the next section we shall state some sufficient conditions ...
Sivu 108
partition with the property that if any one of the separating hyperplanes is removed , at least two nonempty cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 .
partition with the property that if any one of the separating hyperplanes is removed , at least two nonempty cells will merge into one cell . Some examples of nonredundant and redundant partitions are shown in Fig . 6.8 .
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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 |