Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 26
Sivu 20
... exist such that • • · 9 XR are ... , 9R for all X in Xi • • R , ji for all i = 1 , R ( 2.9 ) · " gi ( X ) > gi ( X ) j = 1 ... exists which has each member of X1 on one side and each member of X2 on the other side . Because the decision ...
... exist such that • • · 9 XR are ... , 9R for all X in Xi • • R , ji for all i = 1 , R ( 2.9 ) · " gi ( X ) > gi ( X ) j = 1 ... exists which has each member of X1 on one side and each member of X2 on the other side . Because the decision ...
Sivu 84
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
... exists . ( A similar proof can be given for arbitrary Ŵ1 . ) But , since every pattern in y occurs infinitely often in the training se- quence , termination can occur only if a solution vector is found , which proves the theorem . Other ...
Sivu 87
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
... exists . 5.5 A training theorem for R - category linear machines Suppose we are given R finite subsets of augmented training pattern vec- tors Y1 , Y2 , ... , YR . These subsets are linearly separable if and only if there exist R ...
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 |