Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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... selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced ...
... selection , sometimes called preprocessing . The selection of the measurements or properties on which recognition is based is one of the most important problems in pattern recognition . Yet while there have been many schemes advanced ...
Sivu 4
... selection is a pressing one . Unfortunately , there is very little theory to guide our selection of measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an ...
... selection is a pressing one . Unfortunately , there is very little theory to guide our selection of measurements . At worst this selection process is guided solely by the designer's intuitive ideas about which measurements play an ...
Sivu 8
... selection might be made . · • " 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways . Sometimes they are calculated with precision on the basis of complete a priori knowl- edge about ...
... selection might be made . · • " 1.6 The selection of discriminant functions Discriminant functions can be selected in a variety of ways . Sometimes they are calculated with precision on the basis of complete a priori knowl- edge about ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume 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 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 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 |