A Two-Stage Approach to Domain Adaptation for Statistical Classifiers

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...

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Main Authors:	JIANG, Jing, ZHAI, ChengXiang
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2007
Subjects:	Databases and Information Systems Numerical Analysis and Scientific Computing
Online Access:	https://ink.library.smu.edu.sg/sis_research/1252
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Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-2251
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spelling	sg-smu-ink.sis_research-22512010-12-22T08:24:06Z A Two-Stage Approach to Domain Adaptation for Statistical Classifiers JIANG, Jing ZHAI, ChengXiang 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 2007-11-01T07:00:00Z text https://ink.library.smu.edu.sg/sis_research/1252 info:doi/10.1145/1321440.1321498 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Databases and Information Systems Numerical Analysis and Scientific Computing
spellingShingle	Databases and Information Systems Numerical Analysis and Scientific Computing JIANG, Jing ZHAI, ChengXiang A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
description	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
format	text
author	JIANG, Jing ZHAI, ChengXiang
author_facet	JIANG, Jing ZHAI, ChengXiang
author_sort	JIANG, Jing
title	A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
title_short	A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
title_full	A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
title_fullStr	A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
title_full_unstemmed	A Two-Stage Approach to Domain Adaptation for Statistical Classifiers
title_sort	two-stage approach to domain adaptation for statistical classifiers
publisher	Institutional Knowledge at Singapore Management University
publishDate	2007
url	https://ink.library.smu.edu.sg/sis_research/1252
_version_	1770570930106400768

A Two-Stage Approach to Domain Adaptation for Statistical Classifiers

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