Active Supervised Domain Adaptation

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.

[1]  Hema Raghavan,et al.  Active Learning with Feedback on Features and Instances , 2006, J. Mach. Learn. Res..

[2]  DasguptaSanjoy,et al.  Analysis of Perceptron-Based Active Learning , 2009 .

[3]  Andrew McCallum,et al.  Active Learning by Labeling Features , 2009, EMNLP.

[4]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[5]  Burr Settles,et al.  Active Learning Literature Survey , 2009 .

[6]  Adam Tauman Kalai,et al.  Analysis of Perceptron-Based Active Learning , 2009, COLT.

[7]  James J. Jiang.  A Literature Survey on Domain Adaptation of Statistical Classifiers , 2007 .

[8]  ChengXiang Zhai,et al.  A two-stage approach to domain adaptation for statistical classifiers , 2007, CIKM '07.

[9]  Kagan Tumer,et al.  Estimating the Bayes error rate through classifier combining , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[10]  Motoaki Kawanabe,et al.  Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation , 2007, NIPS.

[11]  Peter A. Flach,et al.  Evaluation Measures for Multi-class Subgroup Discovery , 2009, ECML/PKDD.

[12]  Claire Monteleoni,et al.  Practical Online Active Learning for Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[13]  Hwee Tou Ng,et al.  Domain Adaptation with Active Learning for Word Sense Disambiguation , 2007, ACL.

[14]  Lawrence Carin,et al.  Multi-Task Learning for Classification with Dirichlet Process Priors , 2007, J. Mach. Learn. Res..

[15]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[16]  John Blitzer,et al.  Domain Adaptation with Structural Correspondence Learning , 2006, EMNLP.

[17]  Wei Fan,et al.  Actively Transfer Domain Knowledge , 2008, ECML/PKDD.

[18]  Koby Crammer,et al.  Analysis of Representations for Domain Adaptation , 2006, NIPS.

[19]  Claudio Gentile,et al.  Worst-Case Analysis of Selective Sampling for Linear Classification , 2006, J. Mach. Learn. Res..