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Please use this identifier to cite or link to this item: https://doi.org/10.1109/72.870050
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dc.titleImprovements to the SMO algorithm for SVM regression
dc.contributor.authorShevade, S.K.
dc.contributor.authorKeerthi, S.S.
dc.contributor.authorBhattacharyya, C.
dc.contributor.authorMurthy, K.R.K.
dc.date.accessioned2014-06-17T05:13:59Z
dc.date.available2014-06-17T05:13:59Z
dc.date.issued2000-09
dc.identifier.citationShevade, S.K., Keerthi, S.S., Bhattacharyya, C., Murthy, K.R.K. (2000-09). Improvements to the SMO algorithm for SVM regression. IEEE Transactions on Neural Networks 11 (5) : 1188-1193. ScholarBank@NUS Repository. https://doi.org/10.1109/72.870050
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/58383
dc.description.abstractThis paper points out an important source of inefficiency in Smola and Scholkopf's sequential minimal optimization (SMO) algorithm for support vector machine (SVM) regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression. These modified algorithms perform significantly faster than the original SMO on the datasets tried.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/72.870050
dc.sourceScopus
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.description.doi10.1109/72.870050
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume11
dc.description.issue5
dc.description.page1188-1193
dc.description.codenITNNE
dc.identifier.isiut000089508300014
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