Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 24
... respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean distance d ...
... respect to point sets . i = ངག • Suppose we are given R finite point sets P1 , P2 , . . . , PR . For each .. , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , P ( L ) . Let us define the Euclidean distance d ...
Sivu 26
... respect to point sets is a special case . R 1 1 ( 1 ) = R2 ( 2 ) OR , Ri 2 1 P 1 = 2 1 { Pa ) , p ( 2 ) } FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The structure of Fig . 2-6 ...
... respect to point sets is a special case . R 1 1 ( 1 ) = R2 ( 2 ) OR , Ri 2 1 P 1 = 2 1 { Pa ) , p ( 2 ) } FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The structure of Fig . 2-6 ...
Sivu 135
... respect to point sets , 24 , 121 with respect to points , 16 , 57 Minsky , 62 Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 ...
... respect to point sets , 24 , 121 with respect to points , 16 , 57 Minsky , 62 Model , for a pattern classifier , 7 for a pattern dichotomizer , 8 Modes , 121 estimation of , 123 Mode - seeking training methods , 122 Motzkin , 77 , 78 ...
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 |