Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 23
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | w | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X⚫n + Aw = 0 ( 2.15 ) is said to be ...
... origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | w | . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X⚫n + Aw = 0 ( 2.15 ) is said to be ...
Sivu 67
... origin of weight space . p Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional ... origin of a D - dimensional space . Suppose we have N hyperplanes intersecting at a point ( the origin ) in a D ...
... origin of weight space . p Corresponding to the training subsets X1 and X2 there are subsets of D - dimensional ... origin of a D - dimensional space . Suppose we have N hyperplanes intersecting at a point ( the origin ) in a D ...
Sivu 85
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let W Ŵ2 be the squared distance between some fixed interior point W in W ' and Ŵ ,, where Ŵ ...
... origin but at some point whose distance from the origin increases with increasing M and b . A two- dimensional example is shown in Fig . 5.1 . Let W Ŵ2 be the squared distance between some fixed interior point W in W ' and Ŵ ,, where Ŵ ...
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 |