Classification of incomplete feature vectors by radial basis function networks

被引：12

作者：

Dybowski, R ^{[1
]}

机构：

[1] United Med & Dent Sch Guys & St Thomas Hosp, Dept Microbiol, Sch Med, Intens Care Unit,Div Med,Kings Coll, London SE1 7EH, England

来源：

PATTERN RECOGNITION LETTERS | 1998年 / 19卷 / 14期

关键词：

incomplete data; Gaussian mixture models; radial basis functions; imputation; EM algorithm;

D O I：

10.1016/S0167-8655(98)00096-8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The paper describes the use of radial basis function neural networks with Gaussian basis functions to classify incomplete feature vectors. The method uses the fact that any marginal distribution of a Gaussian distribution can be determined from the mean vector and covariance matrix of the joint distribution. (C) 1998 Published by Elsevier Science B.V. All rights reserved.

引用

页码：1257 / 1264

页数：8

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