Learning Machines: Foundations of Trainable Pattern-classifying Systems |
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Sivu 117
must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the ...
must be organized into R banks . This organization should be regarded as a training problem since it might be unknown beforehand how many subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the ...
Sivu 118
k discriminant functions while leaving their distributions within the banks fixed . Training patterns are presented to the PWL machine ... Such would be the case if the jth bank , j # i , contained the largest subsidiary discriminant .
k discriminant functions while leaving their distributions within the banks fixed . Training patterns are presented to the PWL machine ... Such would be the case if the jth bank , j # i , contained the largest subsidiary discriminant .
Sivu 123
Which of the weight vectors belonging to the ith bank is the closest to X + 1 can now be determined , using the PWL machine ... vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
Which of the weight vectors belonging to the ith bank is the closest to X + 1 can now be determined , using the PWL machine ... vector is adjusted and all other weight vectors ( including all those in the other banks ) are left fixed .
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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 step subsidiary discriminant Suppose terns 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 |
| Alkuperäisteoksen sijainti | Wisconsinin yliopisto - Madison |
| Digitoitu | 16. huhtikuu 2008 |
| ISBN | 0070465703, 9780070465701 |
| Pituus | 137 sivua |
| Vie sitaatti | BiBTeX EndNote RefMan |