Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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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 .
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Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these training methods seems to be more extensive than the theory supporting the nonparametric training methods .
Chapter 3 will investigate decision - theoretic parametric training methods . The mathematical foundation underlying these training methods seems to be more extensive than the theory supporting the nonparametric training methods .
Sivu 65
4 CHAPTER SOME NONPARAMETRIC TRAINING METHODS FOR $ MACHINES 4 : 1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training methods for linear machines ( employing linear discriminant ...
4 CHAPTER SOME NONPARAMETRIC TRAINING METHODS FOR $ MACHINES 4 : 1 Nonparametric training of a TLU In this chapter we shall introduce some specific nonparametric training methods for linear machines ( employing linear discriminant ...
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Sisältö
I | 1 |
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 covariance 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 layered machine linear dichotomies linear machine linearly separable matrix measurements negative 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 solution 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 |