Critical vector learning for text categorisation

UTS Press
Publication Type:
Conference Proceeding
Proceedings 4th Australasion Data Mining Conference AusDM05, 2005, pp. 27 - 36
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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.
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