A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
Conference Proceeding Article
In this paper, we consider the problem of adapting statistical classifiers trained from some source domains where labeled examples are available to a target domain where no labeled example is available. One characteristic of such a domain adaptation problem is that the examples in the source domains and the target domain are known to follow different distributions. Thus a regular classification method would tend to overfit the source domains. We present a two-stage approach to domain adaptation, where at the first
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
16th ACM Conference on Information and Knowledge Management (CIKM'07)
City or Country
JIANG, Jing and ZHAI, ChengXiang.
A Two-Stage Approach to Domain Adaptation for Statistical Classifiers. (2007). 16th ACM Conference on Information and Knowledge Management (CIKM'07). 401-410. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/1252
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