Adaptive Local Hyperplane Classification

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Show simple item record Yang, T Kecman, V 2011-02-07T06:24:48Z 2008-01
dc.identifier.citation Neurocomputing, 2008, 71 (13-15), pp. 3001 - 3004
dc.identifier.issn 0925-2312
dc.identifier.other C1UNSUBMIT en_US
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.
dc.publisher Elsevier BV
dc.relation.isbasedon 10.1016/j.neucom.2008.01.014
dc.title Adaptive Local Hyperplane Classification
dc.type Journal Article
dc.parent Neurocomputing
dc.journal.volume 13-15
dc.journal.volume 71
dc.journal.number 13-15 en_US
dc.publocation Netherlands en_US
dc.identifier.startpage 3001 en_US
dc.identifier.endpage 3004 en_US FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 108195
dc.percentage 100 en_US Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US en_US
dc.location.activity en_US
dc.description.keywords Pattern classification; Nearest neighbor; Manifold en_US
dc.description.keywords Pattern classification
dc.description.keywords Nearest neighbor
dc.description.keywords Manifold
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status Closed Access 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)

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