Active Supervised Domain Adaptation

produced by: Data & Web Mining Lab
author: Parasaran Raman, School of Computing, University of Utah
published: Nov. 30, 2011,   recorded: September 2011,   views: 2625
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Description

In this paper, we harness the synergy between two important learning paradigms, namely, active learning and domain adaptation. We show how active learning in a target domain can leverage information from a different but related source domain. Our proposed framework, Active Learning Domain Adapted (Alda), uses source domain knowledge to transfer information that facilitates active learning in the target domain. We propose two variants of Alda: a batch B-Alda and an online O-Alda. Empirical comparisons with numerous baselines on real-world datasets establish the efficacy of the proposed methods.

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