Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 14
Sivu 81
... fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , W ko = Wk = Wk ko + 1 = ko + 2 is a solution vector . Discussion The value of the fixed correction ...
... fixed - increment error - correction procedure and beginning with any initial weight vector W1 . Then , for some finite index ko , W ko = Wk = Wk ko + 1 = ko + 2 is a solution vector . Discussion The value of the fixed correction ...
Sivu 82
... set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' Y1 = Yk YK = Yk if Yk € Yı if Yk € Y2 ( 5.7 ) The fixed - increment error - correction weight - vector sequence Sw can now ...
... set y ' . Each element of the sequence Sy , is obtained from the corresponding sequence Sy by the relations Yk ' Y1 = Yk YK = Yk if Yk € Yı if Yk € Y2 ( 5.7 ) The fixed - increment error - correction weight - vector sequence Sw can now ...
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 ...
Sisältö
TRAINABLE PATTERN CLASSIFIERS | 1 |
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 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 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 |