| Title: | maximum margin transfer learning |
| Author: | Su Bai
; Shen Yi-Dong
|
| 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
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| Conference Name: | World Summit on Genetic and Evolutionary Computation (GEC 09)
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| Conference Date: | JUN 12-14,
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| Issued Date: | 2009
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| Conference Place: | Shanghai, PEOPLES R CHINA
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| Keyword: | Labels
; Semiconducting germanium compounds
|
| Publisher: | WORLD SUMMIT ON GENETIC AND EVOLUTIONARY COMPUTATION (GEC 09)
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| Publish Place: | 1515 BROADWAY, NEW YORK, NY 10036-9998 USA
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| ISBN: | 978-1-60558-326-6
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| Department: | Su, Bai; Shen, Yi-Dong Chinese Acad Sci, Inst Software, Beijing, Peoples R China.
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| Sponsorship: | ACM SIGEVO
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| 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. |
| Content Type: | 会议论文
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| URI: | http://ir.iscas.ac.cn/handle/311060/8198
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| 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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