Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 36
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
... contain all the points of Z. We shall assume that the points of Z are in general position , meaning , in this case , that no ( K - 2 ) -dimensional hyperplane contains all of them . The set Z The set consists of three points on a line ...
Sivu 102
... contains Y2 and that Y2 contains - Y1 and —Y3 . ) 1 The successive weight vectors are indicated by points and the ap- propriate labels W , ( * ) adjacent to them . The shaded regions indicate those regions that must each contain one of ...
... contains Y2 and that Y2 contains - Y1 and —Y3 . ) 1 The successive weight vectors are indicated by points and the ap- propriate labels W , ( * ) adjacent to them . The shaded regions indicate those regions that must each contain one of ...
Sivu 105
... contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to a vertex in 1 space . Thus , the four ...
... contain pat- tern points . Two of these cells contain one pattern each ; one cell contains four patterns ; and one cell contains two patterns . Each nonempty cell in pattern space corresponds to a vertex in 1 space . Thus , the four ...
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 |