Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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If d can be very large , we might not need to exercise much care in the selection of measurements because it is likely that most of the important ones can be included . But in the more usual and practical cases , in which ( for economic ...
If d can be very large , we might not need to exercise much care in the selection of measurements because it is likely that most of the important ones can be included . But in the more usual and practical cases , in which ( for economic ...
Sivu 28
2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance when we study training procedures .
2.10 Implementation of quadric discriminant functions There are two important methods of implementing quadric discriminant functions . One is suggested by Eq . ( 2 · 21 ) and will be of importance when we study training procedures .
Sivu 29
The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
The other implementation can be derived by studying the properties of the matrix A. This implementation is of somewhat lesser importance and is discussed in detail in the Appendix . To explain the more important implementation we first ...
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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 |