Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 32
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... point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , · · R. Let the symbol R ; denote the set of x2 R 3 R 2 R. -The point ( 5 , -3 ) FIGURE 1.2 Point ...
... point and the pattern vector by the symbol X. A pattern classifier is thus a device which maps the points of Ed into the category numbers , 1 , · · R. Let the symbol R ; denote the set of x2 R 3 R 2 R. -The point ( 5 , -3 ) FIGURE 1.2 Point ...
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... point sets . Suppose we are given R finite point sets P1 , P2 , PR . For each i = 1 , . . . , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , .. , P ( 4 ) . Let us define the Euclidean distance d ( X , P ...
... point sets . Suppose we are given R finite point sets P1 , P2 , PR . For each i = 1 , . . . , R , let the ith point set consist of the L ; points P , ( 1 ) , P , ( 2 ) , .. , P ( 4 ) . Let us define the Euclidean distance d ( X , P ...
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... point sets is a special case . P ( 2 ) ( 1 ) R2 2 p ( 1 ) 2 ( 2 ) 1 R1 ( 1 ) P1 = { P , P2 ) } { pa P = 2 ( 1 ) 2 1 { pl ) , p ( 2 ) } FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 ...
... point sets is a special case . P ( 2 ) ( 1 ) R2 2 p ( 1 ) 2 ( 2 ) 1 R1 ( 1 ) P1 = { P , P2 ) } { pa P = 2 ( 1 ) 2 1 { pl ) , p ( 2 ) } FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
Tekijänoikeudet | |
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assume augmented pattern belonging to category Chapter cluster committee machine committee TLUS correction increment covariance matrix d-dimensional 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 partition pattern classifier pattern hyperplane pattern space pattern vector 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 TLU response training patterns training sequence training set training subsets transformation two-layer machine values W₁ 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 |