Adaptive Local Hyperplane Classification

Yang, T; Kecman, V

Adaptive Local Hyperplane Classification

Yang, T Kecman, V

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Publisher:: Elsevier BV
Publication Type:: Journal Article
Citation:: Neurocomputing, 2008, 71 (13-15), pp. 3001 - 3004
Issue Date:: 2008-01

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Field	Value	Language
dc.contributor.author	Yang, T	en_US
dc.contributor.author	Kecman, V	en_US
dc.date.issued	2008-01	en_US
dc.identifier.citation	Neurocomputing, 2008, 71 (13-15), pp. 3001 - 3004	en_US
dc.identifier.issn	0925-2312	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.publisher	Elsevier BV	en_US
dc.relation.ispartof	Neurocomputing	en_US
dc.relation.isbasedon	10.1016/j.neucom.2008.01.014	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Adaptive Local Hyperplane Classification	en_US
dc.type	Journal Article
utslib.citation.volume	13-15	en_US
utslib.citation.volume	71	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	17 Psychology and Cognitive Sciences	en_US
pubs.embargo.period	Not known	en_US
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
pubs.consider-herdc	false	en_US
pubs.issue	13-15	en_US
pubs.volume	71	en_US

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/13797