Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 21
Sivu 20
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface Si ; is the hyperplane ...
Sivu 39
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2 + ) ( M + 1 ) ...
... Note the pronounced threshold effect , for large M + 1 , around λ = 2 . Also note that for each value of M P2 ( M + 1 ) , M = 1/2 ( 2.45 ) The threshold effect around 2 ( M + 1 ) can be expressed quantitively by lim P ( 2 + ) ( M + 1 ) ...
Sivu 49
... Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 ...
... Note , for example , that the values of the a priori proba- bilities p ( 1 ) and 1 p ( 1 ) affect only the value of wa + 1 . As category 1 becomes less probable a priori , wa + 1 decreases . Such a decrease of wa + 1 favors a category 2 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
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Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |