NDLI: Domain adaptation via support vector machine based on scatter difference

Content Provider	IEEE Xplore Digital Library
Author	Wenhao Ying Conghua Xie Huan Dai Shengrong Gong Jun Wang
Copyright Year	2015
Description	Author affiliation: Sch. of Comput. Sci. & Eng., Changshu Inst. of Technol., Changshu, China (Wenhao Ying; Conghua Xie; Huan Dai; Shengrong Gong) \|\| Sch. of Digital Media, Jiangnan Univ., Wuxi, China (Jun Wang)
Abstract	Domain adaptation methods show better ability to learn when the training data is not identically and independently distributed. The key task of domain adaptation is to find a suitable measure to scale the distributed difference between source domain and target domain. So a projected maximum divergence discrepancy distance measure is proposed. Based on the structural risk minimization theory and the projected maximum divergence discrepancy distance measure, the support vector machine based on difference of divergence is also proposed. Experimental results on artificial and real world problems show the proposed approach could learn across the domains effectively.
Starting Page	27
Ending Page	32
File Size	902322
Page Count	6
File Format	PDF
e-ISBN	9781479998920
DOI	10.1109/ICCAIS.2015.7338675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-29
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines Accuracy Transfer learning Domain adaptation Linear programming Hilbert space Classification algorithms Support vector machine Risk management Difference of divergence Kernel
Content Type	Text
Resource Type	Article

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