Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 9
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
... parameters , some of whose values are unknown . If the values of these parameters were known , ade- quate discriminant functions based on them could be directly specified . In the parametric training methods the training set is used for ...
Sivu 43
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
... parameters ( for example , cluster points ) . The values of these parameters might be unknown a priori . If the parameters were known , we assume that discriminant functions based on them could have been readily specified . Parametric ...
Sivu 44
... parameters of the function p ( X | i ) . The parametric training method for the design of discriminant functions then consists of three steps : 1. The discriminant functions are expressed in terms of the values of the parameters of the ...
... parameters of the function p ( X | i ) . The parametric training method for the design of discriminant functions then consists of three steps : 1. The discriminant functions are expressed in terms of the values of the parameters of the ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X 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 shown in Fig solution weight vectors Stanford 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 |