maximum margin transfer learning

	maximum margin transfer learning
	Su Bai; Shen Yi-Dong
	2009
会议名称	World Summit on Genetic and Evolutionary Computation (GEC 09)
会议录名称	2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC09
会议日期	JUN 12-14,
会议地点	Shanghai, PEOPLES R CHINA
出版地	1515 BROADWAY, NEW YORK, NY 10036-9998 USA
出版者	WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)
ISBN	978-1-60558-326-6
部门归属	Su, Bai; Shen, Yi-Dong Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
摘要	To achieve good generalization in supervised learning, the training and testing examples are usually required to be drawn from the same source distribution. However, in many cases, this identical distribution assumption might be violated when a task from one new domain(target domain) comes, while there are only labeled data from a similar old domain(auxiliary domain). Labeling the new data can be costly and it would also be a waste to throw away all the old data. In this paper, we present a discriminative approach that utilizes the intrinsic geometry of input patterns revealed by unlabeled data, points and derive a maximum-margin formulation of unsupervised transfer learning. Two alternative solutions are proposed to solve the problem. Experimental results on many real data. sets demonstrate the effectiveness and the potential of the proposed methods.
关键词	Labels Semiconducting Germanium Compounds
主办者	ACM SIGEVO
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/8198
专题	基础软件与系统重点实验室
推荐引用方式 GB/T 7714	Su Bai,Shen Yi-Dong. maximum margin transfer learning[C]. 1515 BROADWAY, NEW YORK, NY 10036-9998 USA:WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09),2009.