Bayesian online classifiers for text classification and filtering

Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/40745

DC Field	Value
dc.title	Bayesian online classifiers for text classification and filtering
dc.contributor.author	Chai, K.M.A.
dc.contributor.author	Ng, H.T.
dc.contributor.author	Chieu, H.L.
dc.date.accessioned	2013-07-04T08:11:22Z
dc.date.available	2013-07-04T08:11:22Z
dc.date.issued	2002
dc.identifier.citation	Chai, K.M.A.,Ng, H.T.,Chieu, H.L. (2002). Bayesian online classifiers for text classification and filtering. SIGIR Forum (ACM Special Interest Group on Information Retrieval) : 97-104. ScholarBank@NUS Repository.
dc.identifier.issn	01635840
dc.identifier.uri	http://scholarbank.nus.edu.sg/handle/10635/40745
dc.description.abstract	This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.
dc.source	Scopus
dc.subject	Bayesian
dc.subject	Machine learning
dc.subject	Online
dc.subject	Text classification
dc.subject	Text filtering
dc.type	Conference Paper
dc.contributor.department	COMPUTER SCIENCE
dc.description.sourcetitle	SIGIR Forum (ACM Special Interest Group on Information Retrieval)
dc.description.page	97-104
dc.description.coden	FASRD
dc.identifier.isiut	NOT_IN_WOS
Appears in Collections:	Staff Publications

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