Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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We shall begin by assuming that the pattern classes are characterized by sets of parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that ...
We shall begin by assuming that the pattern classes are characterized by sets of parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that ...
Sivu 43
We shall begin by assuming that the pattern classes are characterized by sets of parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that ...
We shall begin by assuming that the pattern classes are characterized by sets of parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that ...
Sivu 44
We assume that the p ( X \ i ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( Xlc ) , i = 1 , ... , R , are normal probability ...
We assume that the p ( X \ i ) are known functions of a finite number of characteristic parameters whose values we might not know a priori . For example , we may know that the p ( Xlc ) , i = 1 , ... , R , are normal probability ...
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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 |