Interpolating support information granules

被引：10

作者：

Apolloni, Bruno ^{[1
]}

Bassis, Simone ^{[1
]}

Malchiodi, Dario ^{[1
]}

Pedrycz, Witold ^{[2
]}

机构：

[1] Univ Milan, Dept Comp Sci, I-20135 Milan, Italy

[2] Univ Alberta, Dept Elect & Comp Engn, ECERF, Edmonton, AB T6G 2V4, Canada

来源：

NEUROCOMPUTING | 2008年 / 71卷 / 13-15期

关键词：

algorithmic inference; granular computing; linear regression confidence region; modified SVM; kernel methods;

D O I：

10.1016/j.neucom.2007.11.038

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce a regression method that fully exploits both global and local information about a set of points in search of a suitable function explaining their mutual relationships. The points are assumed to form a repository of information granules. At a global level, statistical methods discriminate between regular points and outliers. Then the local component of the information embedded in the former is used to draw an optimal regression curve. We address the challenge of using a variety of standard machine learning tools such as support vector machine (SVM) or slight variants of them within the unifying hat of Granular Computing realm to obtain a definitely new featured nonlinear regression method. The performance of the proposed approach is illustrated with the aid of three well-known benchmarks and ad hoc featured datasets. (C) 2008 Elsevier B.V. All rights reserved.

引用

页码：2433 / 2445

页数：13

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