Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 13
Sivu 49
... depends in a reasonable way on the proba- bilities involved . 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 ...
... depends in a reasonable way on the proba- bilities involved . 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 ...
Sivu 57
... depend on the values of the parameters of the individual probability distributions ; rather , it depends only on the form of the distributions . Even if the parameter values of the distribu- tions , the Σ ; and Mi , are not presently ...
... depend on the values of the parameters of the individual probability distributions ; rather , it depends only on the form of the distributions . Even if the parameter values of the distribu- tions , the Σ ; and Mi , are not presently ...
Sivu 104
... depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second ...
... depends on the values of the weights in the first layer . For a given set of weights , the first layer will transform a finite set X of pattern vectors into a finite set g ( 1 ) of image - space vertices . Now looking at the second ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
11 | 30 |
PARAMETRIC TRAINING METHODS | 43 |
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adjusted apply assume bank called cells changes Chapter classifier cluster column committee machine components Computer 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 terns theorem theory threshold training methods training patterns training procedure training sequence training subsets transformation values weight vectors Y₁ zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |