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| maximum margin transfer learning | |
| Su Bai; Shen Yi-Dong | |
| 2009 | |
| Conference Name | World Summit on Genetic and Evolutionary Computation (GEC 09) |
| Source | 2009 World Summit on Genetic and Evolutionary Computation, 2009 GEC Summit - Proceedings of the 1st ACM/SIGEVO Summit on Genetic and Evolutionary Computation, GEC09 |
| Conference Date | JUN 12-14, |
| Conference Place | Shanghai, PEOPLES R CHINA |
| Publish Place | 1515 BROADWAY, NEW YORK, NY 10036-9998 USA |
| Publisher | WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09) |
| ISBN | 978-1-60558-326-6 |
| Department | Su, Bai; Shen, Yi-Dong Chinese Acad Sci, Inst Software, Beijing, Peoples R China. |
| English Abstract | 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. |
| Keyword | Labels Semiconducting Germanium Compounds |
| Sponsorship | ACM SIGEVO |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/8198 |
| Collection | 基础软件与系统重点实验室 |
| Recommended Citation 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. |
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