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This paper investigates and evaluates support vector machine active learning algorithms for use with imbalanced datasets, which commonly arise in many applications such as information extraction applications.
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M. Bloodgood and J. Grothendieck, “Analysis of stopping active learning based on stabilizing predictions,” in Proceedings of the Seventeenth Conference on Computational Natural Language Learning . Sofia, Bulgaria: Association for Computational Linguistics, August 2013, pp. 10–19
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S. Hantke, Z. Zhang, and B. Schuller, “Towards intelligent crowdsourcing for audio data annotation: Integrating active learning in the real world,” Proc. Interspeech , pp. 3951–3955, 2017
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A. Mishler, K. Wonus, W. Chambers, and M. Bloodgood, “Filtering tweets for social unrest,” in Proceedings of the 2017 IEEE 11th International Conference on Semantic Computing (ICSC) . San Diego, CA, USA: IEEE, January 2017, pp. 17–23. [Online]. Available: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7889498&isnumber=7889486
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G. Beatty, E. Kochis, and M. Bloodgood, “Impact of batch size on stopping active learning for text classification,” in Proceedings of the 2018 IEEE 12th International Conference on Semantic Computing (ICSC) . Laguna Hills, CA, USA: IEEE, January 2018
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S.-W. Lee, D. Zhang, M. Li, M. Zhou, and H.-C. Rim, “Translation model size reduction for hierarchical phrase-based statistical machine translation,” in Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) . Jeju Island, Korea: Association for Computational Linguistics, July 2012, pp. 291–295. [Online]. Available: http://www.aclweb.org/anthology/P12-2057
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Closest in time.