Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 89
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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 12
... pattern - classifying machines has been written by Hawkins.1 Sebestyen2 identifies the task of finding " clustering " transformations as central to the design of pattern classifiers . A paper by Kanal et al.3 contains an excellent ...
... pattern - classifying machines has been written by Hawkins.1 Sebestyen2 identifies the task of finding " clustering " transformations as central to the design of pattern classifiers . A paper by Kanal et al.3 contains an excellent ...
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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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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 |