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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assume belonging to category cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X gi(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors subsets X1 subsidiary discriminant functions Suppose terns TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |