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 Pi1 , Pi2 , Then the linear " Pid . machine of Fig . 2-1 is a minimum - distance classifier with respect to the points P1 , P2 , and ... " PR if the ...
... distance classifier is a linear machine . Suppose that the components of P ; are Pi1 , Pi2 , Then the linear " Pid . machine of Fig . 2-1 is a minimum - distance classifier with respect to the points P1 , P2 , and ... " PR if the ...
Sivu 24
... 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 each ...
... 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 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 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 |