A Two-Stage Approach to Domain Adaptation for Statistical Classifiers

Publication Type

Conference Proceeding Article

Publication Date

11-2007

Abstract

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

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Publication

16th ACM Conference on Information and Knowledge Management (CIKM'07)

First Page

401

Last Page

410

Identifier

10.1145/1321440.1321498

Publisher

ACM

City or Country

Lisbon, Portugal

Additional URL

http://dx.doi.org/10.1145/1321440.1321498

This document is currently not available here.

Share

COinS