Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 22
Sivu 82
... fixed - increment error - correction weight - vector sequence Sw can now be produced recursively from the training sequence Sy by the simplified rule Wk + 1 = Wk Wk + 1 if Y ' k k W > 0 = WK + Yk ' · if Y ' W≤0 k ( 5.8 ) where c is ...
... fixed - increment error - correction weight - vector sequence Sw can now be produced recursively from the training sequence Sy by the simplified rule Wk + 1 = Wk Wk + 1 if Y ' k k W > 0 = WK + Yk ' · if Y ' W≤0 k ( 5.8 ) where c is ...
Sivu 87
... fixed - increment error - correction procedure , suitably modified by replacing zero by T in Eqs . ( 5 · 3 ) and ( 5 · 4 ) , is still guaranteed to produce a solution weight vector when one exists . 5.5 A training theorem for R ...
... fixed - increment error - correction procedure , suitably modified by replacing zero by T in Eqs . ( 5 · 3 ) and ( 5 · 4 ) , is still guaranteed to produce a solution weight vector when one exists . 5.5 A training theorem for R ...
Sivu 134
... Fix , 120 , 125 , 126 Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian ...
... Fix , 120 , 125 , 126 Fix and Hodges method , 120 , 125 Fixed - increment rule , 70 proof of convergence of , 82 , 85 Fractional correction rule , 70 , 91 proof of convergence of , 91 Fundamental training theorem , 79 Gaussian ...
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 |