Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 6
Sivu 87
... weight vector when one exists . 5.5 A training theorem for R - category linear machines Suppose we are given R ... Wi i = 1 , R " ( 5.29 ) We shall prove in this section that a generalization of the fixed- increment error - correction ...
... weight vector when one exists . 5.5 A training theorem for R - category linear machines Suppose we are given R ... Wi i = 1 , R " ( 5.29 ) We shall prove in this section that a generalization of the fixed- increment error - correction ...
Sivu 122
... weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector ... wi , d + 1 = 11⁄2w.w are satisfied . for i = 1 , • · 9 R , j = 1 , · " Li ( 7 · 2 ) If Eq . ( 7 ...
... weight vectors , and the ith bank has L members . Any training method which adjusts the weight vectors in each bank so that each weight vector ... wi , d + 1 = 11⁄2w.w are satisfied . for i = 1 , • · 9 R , j = 1 , · " Li ( 7 · 2 ) If Eq . ( 7 ...
Sivu 123
... weight vectors be selected arbitrarily . * We shall describe the adjustments ... vector in this bank is the closest one to X + 1 . Then , only this closest ... wi [ k + 1 ] is then expressed by w ; [ k + 1 ] = Nw [ k ] + X + 1 N + 1 ( 7.3 ) ...
... weight vectors be selected arbitrarily . * We shall describe the adjustments ... vector in this bank is the closest one to X + 1 . Then , only this closest ... wi [ k + 1 ] is then expressed by w ; [ k + 1 ] = Nw [ k ] + X + 1 N + 1 ( 7.3 ) ...
Sisältö
Preface vii | 11 |
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 important 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 |