Support Vector Machines based on set covering

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dc.contributor.author	Wang Jiaqi	en_US
dc.contributor.author	Zhang Chengqi	en_US
dc.contributor.editor	Shi,F.	en_US
dc.date.accessioned	2009-11-09T05:39:16Z
dc.date.available	2009-11-09T05:39:16Z
dc.date.issued	2004	en_US
dc.identifier	2004001591	en_US
dc.identifier.citation	Wang Jiaqi and Zhang Chengqi 2004, 'Support Vector Machines based on set covering', IEEE, Harbin, China, pp. 181-184.	en_US
dc.identifier.issn	0-646-42313-4	en_US
dc.identifier.other	E1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/3173
dc.description.abstract	Support Vector Machines (SVMs) have been the promising method in the field of machine learning. But for the real applications there are still some drawbacks in SVMs. e.g. the high training cost and too many support vectors. This paper presents a novel method based on set covering to overcome these drawbacks. called SC-SVMs. Some experiments on real data show the effectiveness of this new method.	en_US
dc.publisher	Macquarie Scientific Publishing	en_US
dc.relation.isbasedon		en_US
dc.title	Support Vector Machines based on set covering	en_US
dc.parent	Proceedings of 2nd International Conference on Information Technology and Applications	en_US
dc.journal.volume		en_US
dc.journal.number		en_US
dc.publocation	Sydney, Australia	en_US
dc.identifier.startpage	181	en_US
dc.identifier.endpage	184	en_US
dc.cauo.name	Information Technology	en_US
dc.conference	ICITA 2004	en_US
dc.conference.location	Harbin, China	en_US