Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 17
Sivu 3
... lines and one output line ( see Fig . 1 ∙ 1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 : Pattern ( data to be classified ) Response ( classification ) = 1 or 2 or 3 or or R ...
... lines and one output line ( see Fig . 1 ∙ 1 ) . The d input lines are activated simultaneously by the pattern , and the output line responds * x1 : Pattern ( data to be classified ) Response ( classification ) = 1 or 2 or 3 or or R ...
Sivu 20
... ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface S ;; is the hyperplane which is the perpendicular bisector of the line segment joining ...
... ( lines for d = 2 ) , and that S12 is redundant . In the special case in which the linear machine is a minimum - distance classi- fier , the surface S ;; is the hyperplane which is the perpendicular bisector of the line segment joining ...
Sivu 34
... lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on the same line ) . Select some hyperplane H having an intersection with each of these lines and let these ...
... lines . Because the members of X are in general position , each of these lines is distinct ( i.e. , no three points of X are on the same line ) . Select some hyperplane H having an intersection with each of these lines and let these ...
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 |