Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 9
If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . ... estimates of the parameter values , and the discriminant functions are then determined by these estimates .
If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . ... estimates of the parameter values , and the discriminant functions are then determined by these estimates .
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 ) 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 ( Xi ) , i = 1 , . . . , R , are normal probability - density ...
We assume that the p ( X ) 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 ( Xi ) , i = 1 , . . . , R , are normal probability - density ...
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Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying 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 |