Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 27
Sivu 20
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In S12 the special case in which the linear machine is a minimum - distance classi- fier , the surface S ;; is the hyperplane ...
... Note that the decision surfaces are segments of hyperplanes ( lines for d = 2 ) , and that S12 is redundant . In S12 the special case in which the linear machine is a minimum - distance classi- fier , the surface S ;; is the hyperplane ...
Sivu 23
... Note from Fig . 2.5 that the absolute value of n P is the normal 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 ...
... Note from Fig . 2.5 that the absolute value of n P is the normal 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 ...
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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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 |