Learning Machines: Foundations of Trainable Pattern-classifying SystemsMcGraw-Hill, 1965 - 137 sivua |
Kirjan sisältä
Tulokset 1 - 3 kokonaismäärästä 40
Sivu 16
... machine restricted to employ discriminant functions belonging to a ... linear functions to which we now turn . 2.2 Linear discriminant functions ... machine , termed a linear machine , is depicted in Fig . 2 ∙ 1 . In Fig . 2.1 we employ ...
... machine restricted to employ discriminant functions belonging to a ... linear functions to which we now turn . 2.2 Linear discriminant functions ... machine , termed a linear machine , is depicted in Fig . 2 ∙ 1 . In Fig . 2.1 we employ ...
Sivu 20
... linear machine is a minimum - distance classi- fier , the surface S1 ; is the hyperplane which is the perpendicular bisector of the ... linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
... linear machine is a minimum - distance classi- fier , the surface S1 ; is the hyperplane which is the perpendicular bisector of the ... linear machine are convex 20 SOME IMPORTANT DISCRIMINANT FUNCTIONS Linear classifications of patterns,
Sivu 134
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a hypersphere , 38 for functions , 30 for a quadric ...
... linear machine , 19 , 20 of a piecewise linear machine , 26 , 27 Decision surfaces , 5 , 18 , 19 equation of , 6 , 7 , 18 Decision theory , 44 Degrees of freedom , number of , for a hypersphere , 38 for functions , 30 for a quadric ...
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 |