Improving Generalization with Active Learning
Machine Learning - Special issue on structured connectionist systems
Worst-Case Analysis of Selective Sampling for Linear Classification
The Journal of Machine Learning Research
Actively Transfer Domain Knowledge
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Hierarchical Bayesian domain adaptation
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Domain adaptation for statistical classifiers
Journal of Artificial Intelligence Research
We're not in Kansas anymore: detecting domain changes in streams
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Pointwise prediction for robust, adaptable Japanese morphological analysis
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Relevant knowledge helps in choosing right teacher: active query selection for ranking adaptation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
A weakly-supervised approach to argumentative zoning of scientific documents
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Cross-domain video concept detection: A joint discriminative and generative active learning approach
Expert Systems with Applications: An International Journal
Multi-domain active learning for text classification
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Content-based retrieval for heterogeneous domains: domain adaptation by relative aggregation points
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Batch Mode Active Sampling Based on Marginal Probability Distribution Matching
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on ACM SIGKDD 2012
Active learning for cross-domain sentiment classification
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In this work, we show how active learning in some (target) domain can leverage information from a different but related (source) domain. We present an algorithm that harnesses the source domain data to learn the best possible initializer hypothesis for doing active learning in the target domain, resulting in improved label complexity. We also present a variant of this algorithm which additionally uses the domain divergence information to selectively query the most informative points in the target domain, leading to further reductions in label complexity. Experimental results on a variety of datasets establish the efficacy of the proposed methods.