Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 8
Sivu 23
... Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | w ] . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw ...
... Euclidean distance from the origin to the hyperplane . We shall denote this distance by the symbol Aw , which we set equal to wa + 1 / | w ] . ( If Aw > 0 , the origin is on the positive side of the hyperplane . ) The equation X. n + Aw ...
Sivu 24
... Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by " d ( X , Pi ) = 1 , • i min j = 1 , ... , Li • " XP , ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances between X and ...
... Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by " d ( X , Pi ) = 1 , • i min j = 1 , ... , Li • " XP , ( 2.16 ) That is , the distance between X and P ; is the smallest of the distances between X and ...
Sivu 132
... Euclidean distances . Sebestyen seeks a nondegenerate linear transformation which minimizes the mean square Euclidean distance between pairs of points in a set of points . He finds that the minimizing transformation is Q = D'T ' where T ...
... Euclidean distances . Sebestyen seeks a nondegenerate linear transformation which minimizes the mean square Euclidean distance between pairs of points in a set of points . He finds that the minimizing transformation is Q = D'T ' where T ...
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 |