Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Sivu 12
... contains an excellent formulation of the pattern - classification problem and also points out that many schemes currently attracting the attention of engineers have antecedents in the statistical literature . The problem of data ...
... contains an excellent formulation of the pattern - classification problem and also points out that many schemes currently attracting the attention of engineers have antecedents in the statistical literature . The problem of data ...
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 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
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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adjusted assume augmented pattern belonging to category binary called Chapter cluster committee machine components Cornell Aeronautical Laboratory correction increment covariance matrix d-dimensional decision regions decision surfaces denote density function discussed dot products equal error-correction procedure Euclidean distance example Fix and Hodges fixed-increment error-correction function family g₁(X gi(X given hypersphere image-space implemented initial weight vectors layered machine linear dichotomies linear discriminant functions linearly separable loss function Lx(i mean vector minimum-distance classifier number of linear number of patterns optimum classifier parameters partition pattern classifier pattern hyperplane pattern points pattern space pattern vector pattern-classifying machines patterns belonging Perceptron piecewise linear point sets positive probability distributions prototype pattern PWL machine quadratic form quadric discriminant function quadric function sample covariance matrix solution weight vector Stanford subsets X1 Suppose training patterns training sequence training set training subsets values W₁ wa+1 weight point weight space X₁ X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |