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...
محفوظ في:
| المؤلفون الرئيسيون: | , |
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| التنسيق: | text |
| اللغة: | English |
| منشور في: |
Institutional Knowledge at Singapore Management University
2007
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://ink.library.smu.edu.sg/sis_research/1252 |
| الوسوم: |
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| الملخص: | 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 |
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