Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 15
Sivu 70
... presented for trial . Suppose that the TLU with present weight vector W responds incorrectly to an augmented pattern ... presentation of each pattern is repeated until the pattern is categorized correctly . • In another variation , it is ...
... presented for trial . Suppose that the TLU with present weight vector W responds incorrectly to an augmented pattern ... presentation of each pattern is repeated until the pattern is categorized correctly . • In another variation , it is ...
Sivu 125
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
... presented in this chapter originated with the author , although his opinions were influenced by many . We shall try to give a short account of these influences here . The disadvantage of the error- correction methods , discussed in Sec ...
Sivu 126
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
... presented at 1964 WESCON , August 26-29 , 1964 . 2 Fix , E. , and J. L. Hodges , Jr .: Discriminatory Analysis , Nonparametric Discrimination : Consistency Properties , Project 21-49-004 , Report 4 , prepared at the University of ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges g₁(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 plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix second layer shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns 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 |