Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 9
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 Xn + 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 Xn + Aw ...
Sivu 24
... Euclidean distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by ... " d ( X , P ; ) = i min j = 1 , ... , Li X - P , i ( 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 , P ; ) = i min j = 1 , ... , Li X - P , i ( 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 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 step subsidiary discriminant Suppose terns 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 |