Critical vector learning for text categorisation

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dc.contributor.author Zhang, Lei en_US
dc.contributor.author Zhang, Debbie en_US
dc.contributor.author Simoff, Simeon en_US
dc.contributor.editor Simoff, S; Williams, G; Galloway, J; Volyshkina, I. en_US
dc.date.accessioned 2010-05-18T06:48:12Z
dc.date.available 2010-05-18T06:48:12Z
dc.date.issued 2005 en_US
dc.identifier 2005002856 en_US
dc.identifier.citation Zhang Lei, Zhang Debbie, and Simoff Simeon 2005, 'Critical vector learning for text categorisation', UTS Press, Sydney, Aust, pp. 27-36. en_US
dc.identifier.issn 1-86365-716-9 en_US
dc.identifier.other E1 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. en_US
dc.publisher UTS Press en_US
dc.relation.isbasedon http://www.togaware.com/ausdm05/ en_US
dc.title Critical vector learning for text categorisation en_US
dc.parent Proceedings 4th Australasion Data Mining Conference AusDM05 en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Sydney, Aust en_US
dc.identifier.startpage 27 en_US
dc.identifier.endpage 36 en_US
dc.cauo.name FEIT.School of Systems, Management and Leadership en_US
dc.conference en_US
dc.conference Verified OK en_US
dc.conference.location Sydney, Aust en_US
dc.for 080100 en_US
dc.personcode 10356057 en_US
dc.personcode 020492 en_US
dc.personcode 000716 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.custom Australian Data Mining Conference en_US
dc.date.activity 20051205 en_US
dc.location.activity Sydney, Aust en_US
dc.description.keywords vector learning, support vector mechanisms, text mining en_US
dc.staffid 000716 en_US


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