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 ...
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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 .
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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
Preface vii | 11 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
APPENDIX | 127 |
Tekijänoikeudet | |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial known layered machine linear dichotomies linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space specific Stanford step Suppose theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors 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 |