Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 10
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 f1 , f2 , , fp . Such adjustments ...
... 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 f1 , f2 , , fp . Such adjustments ...
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 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
3 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 step subsidiary discriminant Suppose terns 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 |