Global extremal conditions for multi-integer quadratic programming

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dc.contributorVirginia Techen
dc.contributor.authorWang, Zhenboen
dc.contributor.authorFang, Sue- Cherngen
dc.contributor.authorGao, David Y.en
dc.contributor.authorXing, Wenxunen
dc.contributor.departmentMathematicsen
dc.date.accessed2014-05-09en
dc.date.accessioned2014-05-14T13:23:41Zen
dc.date.available2014-05-14T13:23:41Zen
dc.date.issued2008-05en
dc.description.abstractSupport vector machine (SVM) is a very popular method for binary data classification in data mining ( machine learning). Since the objective function of the unconstrained SVM model is a non-smooth function, a lot of good optimal algorithms can't be used to find the solution. In order to overcome this model's non-smooth property, Lee and Mangasarian proposed smooth support vector machine (SSVM) in 2001. Later, Yuan et al. proposed the polynomial smooth support vector machine (PSSVM) in 2005. In this paper, a three-order spline function is used to smooth the objective function and a three-order spline smooth support vector machine model (TSSVM) is obtained. By analyzing the performance of the smooth function, the smooth precision has been improved obviously. Moreover, BFGS and Newton-Armijo algorithms are used to solve the TSSVM model. Our experimental results prove that the TSSVM model has better classification performance than other competitive baselines.en
dc.description.sponsorshipTsinghua Basic Research Foundation # 052201070en
dc.description.sponsorshipUS NSF Grant # DMI-0553310, CCF-0514768en
dc.identifier.citationWang, Z. B.; Fang, S. C.; Gao, D. Y.; Xing, W. X., "Global extremal conditions for multi-integer quadratic programming," J. Industrial and Management Optimization 4(2), 213-225, (2008); DOI: 10.3934/jimo.2008.4.213en
dc.identifier.doihttps://doi.org/10.3934/jimo.2008.4.213en
dc.identifier.issn1547-5816en
dc.identifier.urihttp://hdl.handle.net/10919/47976en
dc.identifier.urlhttp://www.aimsciences.org/journals/displayArticles.jsp?paperID=3258en
dc.language.isoen_USen
dc.publisherAmerican Institute of Mathematical Sciencesen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectquadratic programmingen
dc.subjectdata miningen
dc.subjectsupport vector machineen
dc.subjectconstrained variational-inequalitiesen
dc.subjectunconstrained optimizationen
dc.subjectcomplementarity-problemsen
dc.subjectglobal optimizationen
dc.subjectperfect dualityen
dc.subjectengineering, multidisciplinaryen
dc.subjectoperations research & managementen
dc.subjectscienceen
dc.subjectmathematics, interdisciplinary applicationsen
dc.titleGlobal extremal conditions for multi-integer quadratic programmingen
dc.title.serialJournal of Industrial and Management Optimizationen
dc.typeArticle - Refereeden

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