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	AN ASYMMETRIC ADAPTIVE CLASSIFICATION METHOD
	Wu, Lianwen ; Cheng, Qiansheng
	2011
关键词	AdaBoost adaptive weight asymmetric error REGRESSION
英文摘要	There should be different requirements for False Reject rate and False Accept rate in classification applications, and classifier learning should use an asymmetric factor to balance between False Reject rate and False Accept rate. A novel AdaBoost algorithm was developed with the asymmetric weight. Moreover we provide the theoretical analysis of its performance and derive the upper bound of the classification error.; Computer Science, Software Engineering; Mathematics, Interdisciplinary Applications; SCI(E); EI; 0; ARTICLE; 1; 169-179; 9
语种	英语
出处	SCI ; EI
出版者	international journal of wavelets multiresolution and information processing
内容类型	其他
源URL	[http://hdl.handle.net/20.500.11897/394921]
专题	数学科学学院
推荐引用方式 GB/T 7714	Wu, Lianwen,Cheng, Qiansheng. AN ASYMMETRIC ADAPTIVE CLASSIFICATION METHOD. 2011-01-01.

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