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Mining Multi-label Data.","key":"e_1_3_2_1_44_1","DOI":"10.1007\/978-0-387-09823-4_34"},{"unstructured":"Brendan Van Rooyen Aditya Menon and Robert C Williamson. 2015. Learning with symmetric label noise: The importance of being unhinged. In Advances in Neural Information Processing Systems. 10--18.  Brendan Van Rooyen Aditya Menon and Robert C Williamson. 2015. Learning with symmetric label noise: The importance of being unhinged. In Advances in Neural Information Processing Systems. 10--18.","key":"e_1_3_2_1_45_1"},{"key":"e_1_3_2_1_46_1","volume-title":"Cong Duy Vu Hoang, and Min-Yen Kan","author":"Wang Aobo","year":"2013","unstructured":"Aobo Wang , Cong Duy Vu Hoang, and Min-Yen Kan . 2013 . Perspectives on crowdsourcing annotations for natural language processing. Language resources and evaluation, Vol. 47 , 1 (2013), 9--31. Aobo Wang, Cong Duy Vu Hoang, and Min-Yen Kan. 2013. Perspectives on crowdsourcing annotations for natural language processing. Language resources and evaluation, Vol. 47, 1 (2013), 9--31."},{"key":"e_1_3_2_1_47_1","volume-title":"Artificial intelligence and collective intelligence. Handbook of Collective Intelligence","author":"Weld Daniel S","year":"2015","unstructured":"Daniel S Weld , Christopher H Lin , and Jonathan Bragg . 2015. Artificial intelligence and collective intelligence. Handbook of Collective Intelligence ( 2015 ), 89--114. Daniel S Weld, Christopher H Lin, and Jonathan Bragg. 2015. Artificial intelligence and collective intelligence. Handbook of Collective Intelligence (2015), 89--114."},{"unstructured":"Peter Welinder Steve Branson Pietro Perona and Serge J Belongie. 2010. The multidimensional wisdom of crowds. In Advances in neural information processing systems. 2424--2432.  Peter Welinder Steve Branson Pietro Perona and Serge J Belongie. 2010. The multidimensional wisdom of crowds. In Advances in neural information processing systems. 2424--2432.","key":"e_1_3_2_1_48_1"},{"volume-title":"Brief survey of crowdsourcing for data mining. Expert Systems with Applications","author":"Xintong Guo","unstructured":"Guo Xintong , Wang Hongzhi , Yangqiu Song , and Gao Hong . [n.d.]. Brief survey of crowdsourcing for data mining. Expert Systems with Applications , Vol. 41 , 17 ( [n.,d.]), 7987--7994. Guo Xintong, Wang Hongzhi, Yangqiu Song, and Gao Hong. [n.d.]. Brief survey of crowdsourcing for data mining. Expert Systems with Applications, Vol. 41, 17 ( [n.,d.]), 7987--7994.","key":"e_1_3_2_1_49_1"},{"key":"e_1_3_2_1_50_1","volume-title":"Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. 932--939","author":"Yan Yan","year":"2010","unstructured":"Yan Yan , R\u00f3mer Rosales , Glenn Fung , Mark Schmidt , Gerardo Hermosillo , Luca Bogoni , Linda Moy , and Jennifer Dy . 2010 . 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