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题名:
maximum margin transfer learning
作者: Su Bai ; Shen Yi-Dong
会议文集: 2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC09
会议名称: World Summit on Genetic and Evolutionary Computation (GEC 09)
会议日期: JUN 12-14,
出版日期: 2009
会议地点: Shanghai, PEOPLES R CHINA
关键词: Labels ; Semiconducting germanium compounds
出版者: WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)
出版地: 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
ISBN: 978-1-60558-326-6
部门归属: Su, Bai; Shen, Yi-Dong Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
主办者: ACM SIGEVO
英文摘要: 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.
内容类型: 会议论文
URI标识: http://ir.iscas.ac.cn/handle/311060/8198
Appears in Collections:计算机科学国家重点实验室 _会议论文

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Recommended Citation:
Su Bai,Shen Yi-Dong. maximum margin transfer learning[C]. 见:World Summit on Genetic and Evolutionary Computation (GEC 09). Shanghai, PEOPLES R CHINA. JUN 12-14,.
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