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 . Suppose we are given R finite point sets P1 , P2 , 9 PR . For each i = 1 , . . . , R , let the ith point set consist of the L ; points P ( 1 ) , P1 ( 2 ) , P ( L ) . Let us define the Euclidean distance d ( X , P ...
... respect to point sets . Suppose we are given R finite point sets P1 , P2 , 9 PR . For each i = 1 , . . . , R , let the ith point set consist of the L ; points P ( 1 ) , P1 ( 2 ) , P ( L ) . Let us define the Euclidean distance d ( X , P ...
Sivu 26
... respect to point sets is a special case . R2 R 1 ހ P ( 1 ) 2 P ( 2 ) 1 ( 1 ) P1 = { P , P2 ) } { p ( " ( 1 ) 1 P2 = { p ) , p ( 2 ) } R1 FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 ...
... respect to point sets is a special case . R2 R 1 ހ P ( 1 ) 2 P ( 2 ) 1 ( 1 ) P1 = { P , P2 ) } { p ( " ( 1 ) 1 P2 = { p ) , p ( 2 ) } R1 FIGURE 2.7 Decision regions for a minimum - distance classifier with respect to the point sets P1 ...
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 |
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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 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 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 |