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

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dc.contributor.author Wen, W en_US
dc.contributor.author Hao, Zhifeng en_US
dc.contributor.author Yang, Xiao en_US
dc.contributor.author Chen, Ming en_US
dc.contributor.editor NA 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.isbasedon http://dx.doi.org/10.1007/11893028_86 en_US
dc.title A heuristic weight-setting algorithm for robust weighted least squares support vector regression en_US
dc.parent International Conference on Neural Information Processing - Lecture Notes in Computer Science en_US
dc.journal.volume 4232 en_US
dc.journal.number en_US
dc.publocation Germany en_US
dc.identifier.startpage 773 en_US
dc.identifier.endpage 781 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000028512 en_US
dc.personcode 0000028511 en_US
dc.personcode 02030548 en_US
dc.personcode 0000030743 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.custom International Conference on Neural Information Processing en_US
dc.date.activity 20061003 en_US
dc.location.activity Hong Kong, China en_US
dc.description.keywords NA en_US
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
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