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(1992, January 27\u201329). Query by Committee. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT \u201992, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130417"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1007\/s10618-016-0469-7","article-title":"Active learning: An empirical study of common baselines","volume":"31","author":"Sharma","year":"2017","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s10994-007-5019-5","article-title":"Active learning for logistic regression: An evaluation","volume":"68","author":"Schein","year":"2007","journal-title":"Mach. Learn."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Lewis, D.D., and Catlett, J. (1994, January 10\u201313). Heterogeneous Uncertainty Sampling for Supervised Learning. Proceedings of the Eleventh International Conference on Machine Learning, New Brunswick, NJ, USA.","DOI":"10.1016\/B978-1-55860-335-6.50026-X"},{"key":"ref_9","unstructured":"Tomanek, K. (2010). Resource-aware Annotation Through Active Learning. [Ph.D. thesis, Technical University Dortmund]."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Dagan, I., and Engelson, S.P. (1995, January 9\u201312). Committee-Based Sampling For Training Probabilistic Classifiers. Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, CA, USA.","DOI":"10.1016\/B978-1-55860-377-6.50027-X"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Settles, B., and Craven, M. (2008, January 25\u201327). An Analysis of Active Learning Strategies for Sequence Labeling Tasks. 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Addressing the Cold Start Problem in Active Learning Approach Used For Semi-automated Sleep Stages Classification. Proceedings of the 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Madrid, Spain.","DOI":"10.1109\/BIBM.2018.8621434"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1109\/TPAMI.2005.159","article-title":"Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy","volume":"8","author":"Peng","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. 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