Robust LS-SVM regression using fuzzy c-means clustering

Authors:
Jooyong Shim;Changha Hwang;Sungkyun Nau
Affiliations:
Department of Applied Statistics, Catholic University of Daegu, Kyungbuk, South Korea;Division of Information and Computer Science, Dankook University, Seoul,Yongsan, South Korea;Division of Information and Computer Science, Dankook University, Seoul,Yongsan, South Korea
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 5
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Least Squares Support Vector Machine Classifiers

Neural Processing Letters
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models

Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Support Vector Machines: Theory and Applications (Studies in Fuzziness and Soft Computing)

Quantified Score

Hi-index	0.00

Visualization

Abstract

The least squares support vector machine(LS-SVM) is a widely applicable and useful machine learning technique for classification and regression. The solution of LS-SVM is easily obtained from the linear Karush-Kuhn-Tucker conditions instead of a quadratic programming problem of SVM. However, LS-SVM is less robust due to the assumption of the errors and the use of a squared loss function. In this paper we propose a robust LS-SVM regression method which imposes the robustness on the estimation of LS-SVM regression by assigning weight to each data point, which represents the membership degree to cluster. In the numerical studies, the robust LS-SVM regression is compared with the ordinary LS-SVM regression.