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dc.contributor.authorÇimen, Emre
dc.contributor.authorÖztürk, Gürkan
dc.contributor.authorGerek, Ömer Nezih
dc.date.accessioned2019-10-21T19:44:02Z
dc.date.available2019-10-21T19:44:02Z
dc.date.issued2018
dc.identifier.issn2352-7110
dc.identifier.urihttps://dx.doi.org/10.1016/j.softx.2017.12.003
dc.identifier.urihttps://hdl.handle.net/11421/19788
dc.descriptionWOS: 000457140200013en_US
dc.description.abstractIncremental Conic Functions (ICF) algorithm is developed for solving classification problems based on mathematical programming. This algorithm improves previous version of conic function-based classifier construction in terms of computational speed. Furthermore, the incremental step avoids the a-priori knowledge of number of sub-classes (which is a necessary parameter in the clustering step of this classification algorithm). Test results show that ICF is, on the average almost 3-times faster than previous versions without sacrificing accuracy. Python 2.7 implementation and software explanations are provided.en_US
dc.description.sponsorshipAnadolu University Scientific Research Projects Commission [1506F499, 1603F122, 1605F524, 1605F435]en_US
dc.description.sponsorshipThis paper is supported by Anadolu University Scientific Research Projects Commission with project numbers 1506F499, 1603F122, 1605F524 and 1605F435.en_US
dc.language.isoengen_US
dc.publisherElsevier Science BVen_US
dc.relation.isversionof10.1016/j.softx.2017.12.003en_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectPolyhedral Conic Functionsen_US
dc.subjectMathematical Programmingen_US
dc.subjectClassificationen_US
dc.subjectMachine Learningen_US
dc.titleICF: An algorithm for large scale classification with conic functionsen_US
dc.typearticleen_US
dc.relation.journalSoftwarexen_US
dc.contributor.departmentAnadolu Üniversitesi, Mühendislik Fakültesien_US
dc.identifier.volume8en_US
dc.identifier.startpage59en_US
dc.identifier.endpage63en_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US]
dc.contributor.institutionauthorGerek, Ömer Nezih


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