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 |
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adjusted apply assume bank belonging to category called changes Chapter cluster committee components consider consists contains correction corresponding decision surfaces define denote density depends derivation described Development discriminant functions discussed distance distribution element equal error-correction estimates example exists expression FIGURE fixed given implemented important initial layered machine linear dichotomies linear discriminant functions linear machine linearly separable measurements negative networks normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector piecewise linear plane points positive presented probability problem proof properties proved PWL machine quadric reduced regions respect response rule sample mean selection separable shown side space Stanford step subsidiary discriminant Suppose theorem theory threshold training methods training procedure training sequence training subsets transformation values weight vectors zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |