Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 16
... classifier employing linear discriminant functions can be simply implemented using weighting and summing devices as discrimina- tors . Such a machine , termed a linear machine , is depicted in Fig . 2.1 . In ... Minimum-distance classifiers,
... classifier employing linear discriminant functions can be simply implemented using weighting and summing devices as discrimina- tors . Such a machine , termed a linear machine , is depicted in Fig . 2.1 . In ... Minimum-distance classifiers,
Sivu 18
... distance classification can be effected by comparing the expressions X. P ; - 1⁄2P ; · P ; for i = 1 , . . . , R and ... minimum - distance classifier is a linear machine . Suppose that the components of P ; are Pi1 , Pi2 , Then the linear " ...
... distance classification can be effected by comparing the expressions X. P ; - 1⁄2P ; · P ; for i = 1 , . . . , R and ... minimum - distance classifier is a linear machine . Suppose that the components of P ; are Pi1 , Pi2 , Then the linear " ...
Sivu 24
... minimum - distance classifier with 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 ...
... minimum - distance classifier with 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 ...
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 |