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	On outlier problem of statistical ensemble learning
	Luo, DS ; Wu, XH ; Chi, HS
	2004
关键词	outlier ensemble learning AdaBoost unit-weighting ALGORITHM MAJORITY
英文摘要	Statistical ensemble learning methods have turned to be effective way to improve accuracy of a learning system [1][2][3]. However, traditional ensemble methods will perform worse if there exist a lot of outliers [4][5][6]. The data selection strategies in traditional ensemble methods are based on the sample-weighting mechanism and lead to the outlier problem serious. In this paper, a new data selection strategy that based on the unit-weighting mechanism is proposed, where the weight of a sample is no longer determined only by the sample itself, it will also be influenced by the unit the sample belongs to. The simulation results on speaker identification using KING benchmark database show that the proposed data selection strategy is effective in dealing with the outliers and successful in improving the identification accuracy.; http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000228622100049&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701 ; Computer Science, Artificial Intelligence; CPCI-S(ISTP); 3
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/293818]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Luo, DS,Wu, XH,Chi, HS. On outlier problem of statistical ensemble learning. 2004-01-01.

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