A new training method for support vector machines: Clustering k-NN support vector machines

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dc.contributor.authorComak, Emre
dc.contributor.authorArslan, Ahmet
dc.date.accessioned2020-03-26T17:26:20Z
dc.date.available2020-03-26T17:26:20Z
dc.date.issued2008
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractFor training of support vector machines (SVMs) efficiently, a new training algorithm, clustering k-NN (k-nearest neighbor) support vector machines (CKSVMs) based on a Gaussian function regulated locally is proposed. In order to reflect degree of training data point as a support vector the Gaussian function is used with k-nearest neighbor (k-NN) method and Euclidean Distance measure. To add local control property to the training algorithm, a simple clustering scheme is implemented before Gaussian functions are constructed for each cluster. In addition, probabilistic SVM outputs are used for extension from binary classification to multi-class classification in pairwise approach. This training algorithm is applied to three commonly used classification problems. Experimental results show that the CKSVM has more classification accuracy than standard multi-class LS-SVM, FLS-SVM and LS-SVM with k-NN method which is proposed in our previous study. In addition to this, the training algorithm highly improved efficiency of the SVM classifier via simple algorithm. (c) 2007 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2007.08.047en_US
dc.identifier.endpage568en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage564en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2007.08.047
dc.identifier.urihttps://hdl.handle.net/20.500.12395/22168
dc.identifier.volume35en_US
dc.identifier.wosWOS:000257993700001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofEXPERT SYSTEMS WITH APPLICATIONSen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectsupport vector machinesen_US
dc.subjectleast squares support vector machinesen_US
dc.subjectGaussian functionsen_US
dc.subjectk-nearest neighboren_US
dc.subjectprobabilistic outputsen_US
dc.titleA new training method for support vector machines: Clustering k-NN support vector machinesen_US
dc.typeReviewen_US

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