Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14365/3975
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dc.contributor.authorOrdin B.-
dc.contributor.authorUylaş N.-
dc.date.accessioned2023-06-16T15:06:32Z-
dc.date.available2023-06-16T15:06:32Z-
dc.date.issued2010-
dc.identifier.isbn9.78996E+12-
dc.identifier.urihttps://hdl.handle.net/20.500.14365/3975-
dc.description24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010 -- 23 June 2010 through 26 June 2010 -- Izmir -- 106702en_US
dc.description.abstractIn this paper we have proposed a semi-supervised algorithm based on quasisecant optimization method for solving data classification problems. The algorithm computes hyperplane(s) to separate two sets with respect to some tolerance. An error function is formulated and an algorithm for its minimization is expressed. We present results of numerical experiments using several UCI test data sets and compare the proposed algorithm with two supervised data classification algorithm (linear separability, max-min separability) and two support vector machine solvers (LIBSVM and SVM-light). © Izmir University of Economics, Turkey, 2010.en_US
dc.language.isoenen_US
dc.publisherVilnius Gediminas Technical Universityen_US
dc.relation.ispartof24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectNonsmooth optimizationen_US
dc.subjectQuasisecant methoden_US
dc.subjectSemi-supervised data classificationen_US
dc.subjectAlgorithmsen_US
dc.subjectOptimizationen_US
dc.subjectSupport vector machinesen_US
dc.subjectData classificationen_US
dc.subjectData classification problemsen_US
dc.subjectLinear separabilityen_US
dc.subjectNonsmooth optimizationen_US
dc.subjectNumerical experimentsen_US
dc.subjectOptimization methoden_US
dc.subjectQuasisecant methoden_US
dc.subjectSemi-supervised algorithmen_US
dc.subjectClassification (of information)en_US
dc.titleLinear separability: Quasisecant method and application to semi-supervised data classificationen_US
dc.typeConference Objecten_US
dc.identifier.scopus2-s2.0-84905454918en_US
dc.authorscopusid35107735000-
dc.identifier.startpage264en_US
dc.identifier.endpage269en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityN/A-
dc.identifier.wosqualityN/A-
item.grantfulltextreserved-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.openairetypeConference Object-
item.fulltextWith Fulltext-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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