Critical vector learning for text categorisation

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dc.contributor.author Zhang, L
dc.contributor.author Zhang, DD
dc.contributor.author Simoff, SJ
dc.contributor.editor Simoff, S
dc.contributor.editor Williams, G
dc.contributor.editor Galloway, J
dc.contributor.editor Volyshkina, I
dc.date.accessioned 2010-05-18T06:48:12Z
dc.date.issued 2005-01
dc.identifier.citation Proceedings 4th Australasion Data Mining Conference AusDM05, 2005, pp. 27 - 36
dc.identifier.isbn 1-86365-716-9
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.
dc.publisher UTS Press
dc.title Critical vector learning for text categorisation
dc.type Conference Proceeding
dc.parent Proceedings 4th Australasion Data Mining Conference AusDM05
dc.journal.number en_US
dc.publocation Sydney, Aust en_US
dc.publocation Sydney, Australia
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 Interactive Entertainment
dc.conference Australian Data Mining Conference
dc.conference.location Sydney, Aust en_US
dc.for 0801 Artificial Intelligence and Image Processing
dc.personcode 000716
dc.personcode 020492
dc.personcode 10356057
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.date.activity 2005-11-23
dc.date.activity 2005-12-05
dc.location.activity Sydney, Aust en_US
dc.location.activity Sydney, Australia
dc.description.keywords vector learning, support vector mechanisms, text mining en_US
dc.description.keywords design tool, human movement, labnotatoion, movement analysis, movement notation, physical interaction
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
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
utslib.copyright.date 2015-04-15 12:23:47.074767+10
utslib.collection.history General (ID: 2)


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