Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 17
Sivu 80
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector depends only on the kth pattern , the correction increment , and the previous weight vector . The weight vector WA is ...
... correction increment c is a positive number , possibly depend- ing on k . That is , the ( k + 1 ) st weight vector depends only on the kth pattern , the correction increment , and the previous weight vector . The weight vector WA is ...
Sivu 81
... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of cÅ is taken to be the smallest integer for which cÂY · Y > │W✩ • Yk❘ . With this pro ...
... correction procedure instead of the fixed - increment error - correction pro- cedure . In the absolute error - correction procedure , the value of cÅ is taken to be the smallest integer for which cÂY · Y > │W✩ • Yk❘ . With this pro ...
Sivu 133
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
... correction train- ing methods , 71 , 72 , 75 of fixed - increment rule , 82 , 85 of fractional correction rule , 91 of generalized error - correction rule , 89 , 90 Convergence theorem , perceptron , 79 Convexity of decision regions ...
Sisältö
Preface vii | 1 |
PARAMETRIC TRAINING METHODS | 43 |
SOME NONPARAMETRIC TRAINING METHODS | 65 |
Tekijänoikeudet | |
4 muita osia ei näytetty
Muita painoksia - Näytä kaikki
Yleiset termit ja lausekkeet
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 |