Critical vector learning for text categorisation

Zhang, L; Zhang, D; Simo, SJ

Critical vector learning for text categorisation

Zhang, L Zhang, D Simo, SJ

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Publication Type:: Conference Proceeding
Citation:: AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005, 2005, pp. 27 - 35
Issue Date:: 2005-12-01

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Field	Value	Language
dc.contributor.author	Zhang, L	en_US
dc.contributor.author	Zhang, D	en_US
dc.contributor.author	Simo, SJ	en_US
dc.date.issued	2005-12-01	en_US
dc.identifier.citation	AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005, 2005, pp. 27 - 35	en_US
dc.identifier.isbn	1863657169	en_US
dc.identifier.isbn	9781863657167	en_US
dc.identifier.uri	http://hdl.handle.net/10453/6745
dc.description.abstract	This paper proposes a new text categorisation method based on the critical vector learning algorithm. By implementing a Bayesian treatment of a generalised linear model of identical function form to the support vector machine, the proposed approach requires signi-cantly fewer support vectors. This leads to much reduced computational com- plexity of the prediction process, which is critical in online applications. © 2013.	en_US
dc.relation	http://purl.org/au-research/grants/arc/DP0557168
dc.relation.ispartof	AusDM 2005 Proc. - 4th Australasian Data Mining Conf. - Collocated with the 18th Australian Joint Conf. on Artificial Intelligence, AI 2005 and the 2nd Australian Conf. on Artifical Life, ACAL 2005	en_US
dc.title	Critical vector learning for text categorisation	en_US
dc.type	Conference Proceeding
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Sydney, Aust	en_US
dc.location.activity	Sydney, Australia
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
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Systems, Management and Leadership
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper proposes a new text categorisation method based on the critical vector learning algorithm. By implementing a Bayesian treatment of a generalised linear model of identical function form to the support vector machine, the proposed approach requires signi-cantly fewer support vectors. This leads to much reduced computational com- plexity of the prediction process, which is critical in online applications. © 2013.

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

http://hdl.handle.net/10453/6745