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...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2007
|
| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1252 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Singapore Management University |
| Language: | English |
| Summary: | 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 |
|---|