A heuristic weight-setting algorithm for robust weighted least squares support vector regression

Wen, W; Hao, Z; Yang, X; Chen, M

A heuristic weight-setting algorithm for robust weighted least squares support vector regression

Wen, W Hao, Z Yang, X Chen, M

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Publisher:: Springer-Verlag
Publication Type:: Journal Article
Citation:: Wen, W. et al. 2001 'A heuristic weight-setting algorithm for robust weighted least squares support vector regression', Lecture Notes in Computer Science, vol. 4232, pp. 750-759.
Issue Date:: 2006

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Field	Value	Language
dc.contributor.author	Wen, W	en_US
dc.contributor.author	Hao, Z	en_US
dc.contributor.author	Yang, X	en_US
dc.contributor.author	Chen, M	en_US
dc.contributor.editor	NA	en_US
dc.date	20061003	en_US
dc.date.accessioned	2009-12-21T02:31:37Z
dc.date.available	2009-12-21T02:31:37Z
dc.date.issued	2006	en_US
dc.identifier	2006006983	en_US
dc.identifier.citation	Wen, W. et al. 2001 'A heuristic weight-setting algorithm for robust weighted least squares support vector regression', Lecture Notes in Computer Science, vol. 4232, pp. 750-759.	en_US
dc.identifier.issn	978-3-540-46479-2	en_US
dc.identifier.other	E1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/4094
dc.description.abstract	Firstly, a heuristic algorithm for labeling the ?outlierness? of samples is presented in this paper. Then based on it, a heuristic weight-setting algorithm for least squares support vector machine (LS-SVM) is proposed to obtain the robust estimations. In the proposed algorithm, the weights are set according to the changes of the observed value in the neighborhood of a sample?s input space. Numerical experiments show that the heuristic weight-setting algorithm is able to set appropriate weights on noisy data and hence effectively improves the robustness of LS-SVM.	en_US
dc.publisher	Springer-Verlag	en_US
dc.relation.ispartof	International Conference on Neural Information Processing - Lecture Notes in Computer Science	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.isbasedon	http://dx.doi.org/10.1007/11893028_86	en_US
dc.subject.classification	Artificial Intelligence and Image Processing	en_US
dc.title	A heuristic weight-setting algorithm for robust weighted least squares support vector regression	en_US
dc.type	Journal article
utslib.citation.volume	4232	en_US
utslib.location	Germany	en_US
utslib.citation.startpage	773	en_US
utslib.citation.endpage	781	en_US
utslib.identifier.org	FEIT.School of Systems, Management and Leadership	en_US
utslib.for	080100	en_US
utslib.percentage	100	en_US
dc.location.activity	Hong Kong, China	en_US
utslib.copyright.status	closed_access
pubs.declined	1970-01-01T00:00:00.0+1000

Abstract:

Firstly, a heuristic algorithm for labeling the ?outlierness? of samples is presented in this paper. Then based on it, a heuristic weight-setting algorithm for least squares support vector machine (LS-SVM) is proposed to obtain the robust estimations. In the proposed algorithm, the weights are set according to the changes of the observed value in the neighborhood of a sample?s input space. Numerical experiments show that the heuristic weight-setting algorithm is able to set appropriate weights on noisy data and hence effectively improves the robustness of LS-SVM.

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http://hdl.handle.net/10453/4094