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An adaptive approach to learning optimal neighborhood kernels

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dc.contributor.authorLIU, Xinwang
dc.contributor.authorYin, Jianping
dc.contributor.authorWang, Lei
dc.contributor.authorLiu, Lingqiao
dc.contributor.authorLiu, J
dc.contributor.authorHou, Chenping
dc.contributor.authorZhang, Jian
dc.date.accessioned2015-12-13T22:33:09Z
dc.date.issued2013
dc.date.updated2015-12-11T09:14:13Z
dc.description.abstractLearning an optimal kernel plays a pivotal role in kernel-based methods. Recently, an approach called optimal neighborhood kernel learning (ONKL) has been proposed, showing promising classification performance. It assumes that the optimal kernel will resi
dc.identifier.issn2168-2267
dc.identifier.urihttp://hdl.handle.net/1885/75889
dc.publisherIEEE
dc.sourceIEEE Transactions on Cybernetics
dc.titleAn adaptive approach to learning optimal neighborhood kernels
dc.typeJournal article
local.bibliographicCitation.issue1
local.bibliographicCitation.lastpage384
local.bibliographicCitation.startpage371
local.contributor.affiliationLIU, Xinwang, College of Engineering and Computer Science, ANU
local.contributor.affiliationYin, Jianping, National University of Defense Technology
local.contributor.affiliationWang, Lei, University of Wollongong
local.contributor.affiliationLiu, Lingqiao, College of Engineering and Computer Science, ANU
local.contributor.affiliationLiu, J, Siemens Corporate Research
local.contributor.affiliationHou, Chenping, National University of Defense Technology
local.contributor.affiliationZhang, Jian, University of Technology Sydney
local.contributor.authoruidLIU, Xinwang, u4811004
local.contributor.authoruidLiu, Lingqiao, u4629919
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.identifier.absfor080309 - Software Engineering
local.identifier.ariespublicationf5625xPUB4841
local.identifier.citationvolume43
local.identifier.doi10.1109/TSMCB.2012.2207889
local.identifier.scopusID2-s2.0-84890442548
local.type.statusPublished Version

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