Critical vector learning for text categorisation

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
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2005002856.pdf665.98 kB
Adobe PDF
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: