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
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
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assume belonging to category Chapter cluster committee machine committee TLUS components correction increment covariance matrix decision surfaces denote diagonal matrix discussed dot products error-correction procedure Euclidean distance example Fix and Hodges function g(X g₁(X given Hodges method hypersphere image-space implemented initial weight vectors ith bank layer of TLUS layered machine linear dichotomies linear discriminant functions linearly separable loss function mean vector minimum-distance classifier mode-seeking networks nonparametric number of patterns p₁ parameters parametric training partition pattern hyperplane pattern points pattern space pattern vector pattern-classifying patterns belonging perceptron piecewise linear plane point sets positive probability distributions prototype pattern PWL machine quadratic form quadric function rule sample covariance matrix shown in Fig solution weight vectors Stanford subsets X1 subsidiary discriminant functions Suppose terns training patterns training sequence training set training subsets transformation two-layer machine values W₁ wa+1 weight point weight space weight-vector sequence X1 and X2 zero
Viitteet tähän teokseen
A Probabilistic Theory of Pattern Recognition Luc Devroye,László Györfi,Gabor Lugosi Rajoitettu esikatselu - 1997 |