Adaptive Local Hyperplane Classification

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dc.contributor.author Yang, Tao en_US
dc.contributor.author Kecman, Vojislav en_US
dc.contributor.editor en_US
dc.date.accessioned 2011-02-07T06:24:48Z
dc.date.available 2011-02-07T06:24:48Z
dc.date.issued 2008 en_US
dc.identifier 2009002164 en_US
dc.identifier.citation Yang Tao and Kecman Vojislav 2008, 'Adaptive Local Hyperplane Classification', Elsevier BV, vol. 71, no. 13-15, pp. 3001-3004. en_US
dc.identifier.issn 0925-2312 en_US
dc.identifier.other C1UNSUBMIT en_US
dc.identifier.uri http://hdl.handle.net/10453/13797
dc.description.abstract In this paper, a novel classifier, called adaptive local hyperplane, is proposed for pattern classification. The experimental results on 11 real data sets demonstrate that the proposed classifier outperforms, on average, all the other seven benchmarking classifiers. In particular, it is the best classifier in 10 out of 11 data sets, and it is the close second best for just one data set. en_US
dc.language en_US
dc.publisher Elsevier BV en_US
dc.relation.isbasedon http://dx.doi.org/10.1016/j.neucom.2008.01.014 en_US
dc.title Adaptive Local Hyperplane Classification en_US
dc.parent Neurocomputing en_US
dc.journal.volume 71 en_US
dc.journal.number 13-15 en_US
dc.publocation Netherlands en_US
dc.identifier.startpage 3001 en_US
dc.identifier.endpage 3004 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 108195 en_US
dc.personcode 0000059430 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Pattern classification; Nearest neighbor; Manifold en_US
dc.staffid en_US


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