Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 13
Sivu 82
... set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' = Yk if Yk € Yı YK = Yk ' Yk k if Y * € Y2 ( 5.7 ) The fixed - increment error - correction weight - vector sequence Sw can ...
... set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' = Yk if Yk € Yı YK = Yk ' Yk k if Y * € Y2 ( 5.7 ) The fixed - increment error - correction weight - vector sequence Sw can ...
Sivu 87
... fixed- increment error - correction training procedure will produce a set of R solution weight vectors ( and thus a set of R discriminant functions ) for linearly separable training subsets . We define a training sequence Sy on the training ...
... fixed- increment error - correction training procedure will produce a set of R solution weight vectors ( and thus a set of R discriminant functions ) for linearly separable training subsets . We define a training sequence Sy on the training ...
Sivu 90
... fixed - increment error - correction pro- cedure using the sequence Sz and starting with an initial weight vector V1 = [ Ŵ1 ( 1 ) , Ŵ2 ( 1 ) , WR ) ] . The sequence Sz can be regarded as a reduced training sequence of the patterns in Z ...
... fixed - increment error - correction pro- cedure using the sequence Sz and starting with an initial weight vector V1 = [ Ŵ1 ( 1 ) , Ŵ2 ( 1 ) , WR ) ] . The sequence Sz can be regarded as a reduced training sequence of the patterns in Z ...
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 |