Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
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Tulokset 1 - 3 kokonaismäärästä 17
Sivu 100
... negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( k ) , W2 ) , ... , and Wp ) . In describing the rule for modifying the weight vectors we shall make use of the notation Р Nx = Σ sgn ( W , ( * ) . Yk ) ...
... negative dot products with Y. Let the weight vec- tors at this stage be given by W1 ( k ) , W2 ) , ... , and Wp ) . In describing the rule for modifying the weight vectors we shall make use of the notation Р Nx = Σ sgn ( W , ( * ) . Yk ) ...
Sivu 101
... negative ( but not 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 ...
... negative ( but not 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 ...
Sivu 127
... negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A ∙ 1 ) where A is a real , d × d , symmetric matrix , B is a d - dimensional column vector , and C is a scalar . The first term on the right - hand side of Eq ...
... negative parts Consider the quadric function g ( X ) = X'AX + B'X + C ( A ∙ 1 ) where A is a real , d × d , symmetric matrix , B is a d - dimensional column vector , and C is a scalar . The first term on the right - hand side of Eq ...
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 |