Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 10
Sivu 16
... classifier employing linear discriminant functions can be simply implemented using weighting and summing devices as discrimina- tors . Such a machine , termed a linear machine , is depicted in Fig . 2.1 . In ... Minimum-distance classifiers,
... classifier employing linear discriminant functions can be simply implemented using weighting and summing devices as discrimina- tors . Such a machine , termed a linear machine , is depicted in Fig . 2.1 . In ... Minimum-distance classifiers,
Sivu 18
... distance classification can be effected by comparing the expressions X. P ; 1⁄2P ; P ; for i = 1 , . . . , R and ... minimum - distance classifier is a linear machine . Suppose that the components of P ; are pil , Pi2 , . . . , Pid . Then the ...
... distance classification can be effected by comparing the expressions X. P ; 1⁄2P ; P ; for i = 1 , . . . , R and ... minimum - distance classifier is a linear machine . Suppose that the components of P ; are pil , Pi2 , . . . , Pid . Then the ...
Sivu 24
... minimum - distance classifier with respect to point sets . ... 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 ...
... minimum - distance classifier with respect to point sets . ... 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 ...
Sisältö
Preface vii | 11 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern hyperplane pattern space pattern vector pattern-classifying piecewise linear plane points positive presented probability problem properties PWL machine quadric regions respect response rule selection separable sequence side solution space Stanford step subsidiary discriminant Suppose terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |