Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 87
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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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks 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 space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods 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 |