Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 18
Sivu 24
... respect to point sets . i = 1 ,. 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 ( X , P ...
... respect to point sets . i = 1 ,. 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 ( X , P ...
Sivu 26
... respect to point sets is a special case . R1 འ R2 ( 2 ) R1 FIGURE 2.7 ( 1 ) P1 = { P ) , P ( 2 ) } P = 2 { P ( 1 ) , p ( 2 ) } 2 P Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The structure ...
... respect to point sets is a special case . R1 འ R2 ( 2 ) R1 FIGURE 2.7 ( 1 ) P1 = { P ) , P ( 2 ) } P = 2 { P ( 1 ) , p ( 2 ) } 2 P Decision regions for a minimum - distance classifier with respect to the point sets P1 , P2 The structure ...
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 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies 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 classifier pattern hyperplane pattern space pattern vector 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 theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors X1 and X2 Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |