Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 92
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... machines , those which can be trained to recognize patterns . Some well - known examples of trainable pattern - classifying systems are the PERCEPTRON and the MADALINE and MINOS networks . The subject of trainable pattern - classifying ...
... machines , those which can be trained to recognize patterns . Some well - known examples of trainable pattern - classifying systems are the PERCEPTRON and the MADALINE and MINOS networks . The subject of trainable pattern - classifying ...
Sivu 31
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. developed for linear discriminant functions , but by the above consider- ations we can extend the application of these results to the whole class of functions . We ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. developed for linear discriminant functions , but by the above consider- ations we can extend the application of these results to the whole class of functions . We ...
Sivu 116
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. whether a given pattern is closer to the point set P1 than it is to the point set P2 and can accordingly place the pattern in category 1 or category 2 . Such a ...
Foundations of Trainable Pattern-classifying Systems Nils J. Nilsson. whether a given pattern is closer to the point set P1 than it is to the point set P2 and can accordingly place the pattern in category 1 or category 2 . Such a ...
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 |