Please use this identifier to cite or link to this item: https://doi.org/10.1109/72.822516
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dc.titleA fast iterative nearest point algorithm for support vector machine classifier design
dc.contributor.authorKeerthi, S.S.
dc.contributor.authorShevade, S.K.
dc.contributor.authorBhattacharyya, C.
dc.contributor.authorMurthy, K.R.K.
dc.date.accessioned2014-06-16T09:27:45Z
dc.date.available2014-06-16T09:27:45Z
dc.date.issued2000-01
dc.identifier.citationKeerthi, S.S., Shevade, S.K., Bhattacharyya, C., Murthy, K.R.K. (2000-01). A fast iterative nearest point algorithm for support vector machine classifier design. IEEE Transactions on Neural Networks 11 (1) : 124-136. ScholarBank@NUS Repository. https://doi.org/10.1109/72.822516
dc.identifier.issn10459227
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/54135
dc.description.abstractIn this paper we give a new fast iterative algorithm for support vector machine (SVM) classifier design. The basic problem treated is one that does not allow classification violations. The problem is converted to a problem of computing the nearest point between two convex polytopes. The suitability of two classical nearest point algorithms, due to Gilbert, and Mitchell et al., is studied. Ideas from both these algorithms are combined and modified to derive our fast algorithm. For problems which require classification violations to be allowed, the violations are quadratically penalized and an idea due to Cortes and Vapnik and Frieß is used to convert it to a problem in which there are no classification violations. Comparative computational evaluation of our algorithm against powerful SVM methods such as Platt's sequential minimal optimization shows that our algorithm is very competitive. © 2000 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/72.822516
dc.sourceScopus
dc.subjectClassification
dc.subjectNearest point algorithm
dc.subjectQuadratic programming
dc.subjectSupport vector machine
dc.typeArticle
dc.contributor.departmentMECHANICAL & PRODUCTION ENGINEERING
dc.description.doi10.1109/72.822516
dc.description.sourcetitleIEEE Transactions on Neural Networks
dc.description.volume11
dc.description.issue1
dc.description.page124-136
dc.description.codenITNNE
dc.identifier.isiut000085524000013
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