Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 11
Sivu 112
... discriminant functions defined by Eq . ( 6.16 ) are com- prised of pieces of a number of " subsidiary " discriminant , functions . These subsidiary functions are the gi ( X ) and the g2 ( X ) . Examination of Eq . ( 6.13 ) reveals that ...
... discriminant functions defined by Eq . ( 6.16 ) are com- prised of pieces of a number of " subsidiary " discriminant , functions . These subsidiary functions are the gi ( X ) and the g2 ( X ) . Examination of Eq . ( 6.13 ) reveals that ...
Sivu 117
... subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the subsidiary ... discriminant functions ; their adjustment is accomplished by adjusting the linear functions fi , f2 , . . , fp . Such ...
... subsidiary discriminators should be in each bank . Thus , training also in- volves shuffling the subsidiary ... discriminant functions ; their adjustment is accomplished by adjusting the linear functions fi , f2 , . . , fp . Such ...
Sivu 118
... subsidiary linear discriminant functions have initially been se- lected arbitrarily . After presenting a pattern which the machine classifies correctly , we make no changes in the values of the weights used to imple- ment the subsidiary ...
... subsidiary linear discriminant functions have initially been se- lected arbitrarily . After presenting a pattern which the machine classifies correctly , we make no changes in the values of the weights used to imple- ment the subsidiary ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
TRAINING THEOREMS | 79 |
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adjusted apply assume bank called cells changes Chapter cluster column committee machine components consider consists contains correction corresponding covariance decision surfaces define denote density depends described dichotomies discriminant functions discussed distance distributions elements equal error-correction estimates example exist expression FIGURE fixed given implemented important initial layered machine linear machine linearly separable lines majority matrix mean measurements modes negative networks nonparametric normal Note optimum origin parameters partition pattern classifier pattern hyperplane pattern space pattern vector 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 |