Abstract
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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Saha, A., Rai, P., Daumé, H., Venkatasubramanian, S., DuVall, S.L. (2011). Active Supervised Domain Adaptation. In: Gunopulos, D., Hofmann, T., Malerba, D., Vazirgiannis, M. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2011. Lecture Notes in Computer Science(), vol 6913. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23808-6_7
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DOI: https://doi.org/10.1007/978-3-642-23808-6_7
Publisher Name: Springer, Berlin, Heidelberg
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