Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 23
Sivu 28
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
... positive definite . If A has one or more of its eigenvalues equal to zero and all the others positive , then the quadratic form will never be negative , and it and A are called positive semidefinite . 2.9 Quadric decision surfaces The ...
Sivu 69
... positive number called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly ...
... positive number called the correction increment . It controls the extent of the adjustment . For sufficiently large c , the weight point will cross the pattern hyperplane , and Y. W ' will be correctly positive . If W were incorrectly ...
Sivu 101
... positive ) dot products with Y. If the weight vector W ( ) is among this set of 1⁄2 ( | N | + 1 ) weight vectors , it is adjusted by the rule W1 ( k + 1 ) = W ( k ) + c ; ( k ) Yk ( 6 · 7 ) where c ) is the correction increment which we ...
... positive ) dot products with Y. If the weight vector W ( ) is among this set of 1⁄2 ( | N | + 1 ) weight vectors , it is adjusted by the rule W1 ( k + 1 ) = W ( k ) + c ; ( k ) Yk ( 6 · 7 ) where c ) is the correction increment which we ...
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 |