Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 66
... Weight space Before discussing training methods for a TLU it will be helpful to formu- late a geometric representation in which the TLU weight values are the coordinates of a point in a multidimensional space . This space , which we ...
... Weight space Before discussing training methods for a TLU it will be helpful to formu- late a geometric representation in which the TLU weight values are the coordinates of a point in a multidimensional space . This space , which we ...
Sivu 67
... point representing the weight values w1 = 0 , w2 = 0 , . . . , WD = 0 satisfies Eq . ( 4 · 2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . p Corresponding to the training subsets X1 and ...
... point representing the weight values w1 = 0 , w2 = 0 , . . . , WD = 0 satisfies Eq . ( 4 · 2 ) regard- less of Y. Therefore all pattern hyperplanes pass through the origin of weight space . p Corresponding to the training subsets X1 and ...
Sivu 68
... weight points W satisfying inequality ( 4.3 ) . = These ideas are illustrated in Fig . 4.1 for a two - dimensional ... point W , Solution region 2 W Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern ...
... weight points W satisfying inequality ( 4.3 ) . = These ideas are illustrated in Fig . 4.1 for a two - dimensional ... point W , Solution region 2 W Pattern hyperplanes FIGURE 4.1 A two - dimensional weight space with three pattern ...
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 |