Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 20
... X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that · • 9 XR are gi ( X ) ... of x into two subsets X1 and X2 is a linear only if a linear discriminant function g exists such that 2.
... X2 , linearly separable if and only if linear discriminant functions g1 , 92 , ... , gr exist such that · • 9 XR are gi ( X ) ... of x into two subsets X1 and X2 is a linear only if a linear discriminant function g exists such that 2.
Sivu 84
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Other than the fact that a bound on the number of steps exists , thus proving the theorem , the bound itself is not very useful in estimating how many steps will be required in a given situation , since it depends on knowledge of a ...
Sivu 97
There do not yet exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) Therefore ...
There do not yet exist , how- ever , efficient adjustment rules for such thorough training of a layered machine . ... That is , no vector W exists such that and Y. W > 0 for each Y in Yı Y. W < 0 for each Y in Y2 ( 6.1 ) Therefore ...
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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 |