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dc.contributorVirginia Tech
dc.contributor.authorWang, Z. B.
dc.contributor.authorFang, S. C.
dc.contributor.authorGao, D. Y.
dc.contributor.authorXing, W. X.
dc.date.accessioned2014-05-14T13:23:41Z
dc.date.available2014-05-14T13:23:41Z
dc.date.issued2008-05
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.2008.4.213
dc.identifier.issn1547-5816
dc.identifier.urihttp://hdl.handle.net/10919/47976
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.
dc.description.sponsorshipTsinghua Basic Research Foundation # 052201070
dc.description.sponsorshipUS NSF Grant # DMI-0553310, CCF-0514768
dc.language.isoen_US
dc.publisherAmerican Institute of Mathematical Sciences
dc.subjectquadratic programming
dc.subjectdata mining
dc.subjectsupport vector machine
dc.subjectconstrained variational-inequalities
dc.subjectunconstrained optimization
dc.subjectcomplementarity-problems
dc.subjectglobal optimization
dc.subjectperfect duality
dc.subjectengineering, multidisciplinary
dc.subjectoperations research & management
dc.subjectscience
dc.subjectmathematics, interdisciplinary applications
dc.titleGlobal extremal conditions for multi-integer quadratic programming
dc.typeArticle
dc.identifier.urlhttp://www.aimsciences.org/journals/displayArticles.jsp?paperID=3258
dc.date.accessed2014-05-09
dc.title.serialJournal of Industrial and Management Optimization
dc.identifier.doi10.3934/jimo.2008.4.213�


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