Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 75
... belonging to category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every pattern in y . A response to a pattern in category i is correct only if the ith discriminant is the ...
... belonging to category i . We desire to train the linear machine by adjusting its weight vectors so that it responds correctly to every pattern in y . A response to a pattern in category i is correct only if the ith discriminant is the ...
Sivu 116
... category . Suppose that there are L ; prototype patterns for the ith category and that all patterns belonging to category i are close to one of these prototypes . Then , a PWL machine with L ; linear discrimi- nators in the Lith bank ...
... category . Suppose that there are L ; prototype patterns for the ith category and that all patterns belonging to category i are close to one of these prototypes . Then , a PWL machine with L ; linear discrimi- nators in the Lith bank ...
Sivu 121
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category having the ...
... belonging to cate- gory 1 , L2 belonging to category 2 , etc. Then , given these modes , one reasonable way to classify some arbi- trary pattern X is to measure its distance to each of the modes and place it in that category having the ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |