Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 24
Sivu 18
... distance classifier is a linear machine . Suppose that the components of P ; are pil , Pi2 , . . . , Pid . Then the linear machine of Fig . 2 · 1 is a minimum - distance classifier with respect to the points P1 , P2 , . . . , PR if the ...
... distance classifier is a linear machine . Suppose that the components of P ; are pil , Pi2 , . . . , Pid . Then the linear machine of Fig . 2 · 1 is a minimum - distance classifier with respect to the points P1 , P2 , . . . , PR if the ...
Sivu 24
... distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by " i d ( X , P ; ) = min j = 1 , ... , Li - | X – P1 ( ) | ( 2 · 16 ) That is , the distance between X and P ; is the smallest of the distances between X and each ...
... distance d ( X , P ; ) from an arbi- trary point X to the point set P ; by " i d ( X , P ; ) = min j = 1 , ... , Li - | X – P1 ( ) | ( 2 · 16 ) That is , the distance between X and P ; is the smallest of the distances between X and each ...
Sivu 121
... distance to each of the modes and place it in that category having the nearest mode . But this procedure is just a minimum - distance - classification rule with respect to point sets . The points belonging to the ith point set P ; are ...
... distance to each of the modes and place it in that category having the nearest mode . But this procedure is just a minimum - distance - classification rule with respect to point sets . The points belonging to the ith point set P ; are ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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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 |