Journal of Shandong University(Engineering Science) ›› 2022, Vol. 52 ›› Issue (3): 18-24.doi: 10.6040/j.issn.1672-3961.0.2021.318
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WANG Li, YU Mingqian, LIU Wenpeng, ZHOU Yu, ZHENG Ruirui, HE Jianjun*
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| [1] COUR T, SAPP B, TASKAR B. Learning from partial labels[J]. The Journal of Machine Learning Research, 2011, 12: 1501-1536. [2] COUR T, SAPP B, JORDAN C, et al. Learning from ambiguously labeled images[C] //Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA:IEEE, 2009: 919-926. [3] JIN R, GHAHRAMANI Z. Learning with multiple labels[J].Advances in Neural Information Processing Systems, 2002, 3(2):921-928. [4] COME E, OUKHELLOU L. Learning from partially supervised data using mixture models and belief functions[J]. Pattern Recognition, 2009, 42(3):334-348. [5] LIU L P, DIETTERICH T G.A Conditional multinomial mixture model for superset label learning[C] //Proceedings of the Advances in Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc, 2012:557-565. [6] LIU L P, DIETTERICH T G. Leainability of the superset label learning problem[C] //Proceedings of the 31st International Conference on Machine Learning. Beijing, China: JMLR, 2014:1629-1637. [7] HULLERMERIER E, BERINGER J. Learning from ambiguously labeled examples[J]. Intelligent Data Analysis, 2006, 10(5): 419-439. [8] 周斌斌.基于问题转换的偏标记学习算法研究[D].南京:东南大学自动化系,2017. ZHOU Binbin. Research on partial label learning algorithm[D]. Nanjing: School of Automation, Southeast University, 2017. [9] ZENG Z, XIAO S, JIA K, et al. Learning by associating ambiguously labeled images[C] //Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition.California,USA:IEEE, 2013:708-715. [10] JIE L, ORABONA F. Learning from candidate labeling sets[C] //Advances in Neural Information Processing Systems 23. Cambridge, USA:MIT Press, 2010:1504-1512. [11] ZHANG M L, ZHOU B B, LIU X Y. Partial label learning via feature-aware disambiguation[C] //Proceedings of the 22nd ACM SIGKDDInternational Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2016:1335-1344. [12] CHEN Y C, PATEL V M, CHELLAPPA R, et al. Ambiguously labeled learning using dictionaries[J]. IEEE Transactions on Information Forensics and Security, 2014, 9(12): 2076-2088. [13] GONG C, LIU T, TANG Y, et al. A regularization approach for instance-based superset label learning[J]. IEEE Transactions on Cybernetics, 2017, 48(3): 967-978. [14] LUO J, ORABONA F. Learning from candidate labeling sets[J]. Advances in Neural Information Processing Systems, 2010, 23(3):295-299. [15] BEYGELZIMER A, LANGFORD J. The offset tree for learning with partial labels[C] //Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2009: 129-138. [16] C(^overO)ME E, OUKHELLOU L, DENOEUX T, et al. Learning from partially supervised data using mixture models and belief functions[J]. Pattern Recognition, 2009, 42(3): 334-348. [17] TANG C Z, ZHANG M L. Confidence-rated discriminative partial label learning[C] //Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence. San Francisco, California, USA: AAAI Press, 2017: 2611-2617. [18] ZHOU Y, HE J, GU H. Partial label learning via gaussian processes[J]. Cybernetics, IEEE Transactions on, 2017, 47(12):4443-4450. [19] YE Zhifei, WEN Yimin, LÜ Baoliang. A survey of imbalanced pattern classification problems[J]. China Association of Artificial Intelligence Transcation Intelligent Systems, 2009, 4(2): 148-156. [20] LI S, WANG Z, ZHOU G, et al. Semi-supervised learning for imbalanced sentiment classification[J]. Plor, 2011, 4(l):1826-1831. [21] CHEN E, LIN Y, XIONG H, et al. Exploiting probabilistic topic models to improve text categorization under class imbalance[J].Information Processing & Management, 2011, 47(2): 202-214. [22] LUSA L, BLAGUS R. The class-imbalance problem for high-dimensional class prediction[C] //Proceedings of 2012 11th International Conference on Machine Learning and Applications. Boca Raton, FL, USA: IEEE, 2012:123-126. [23] WANG J, ZHANG M L. Towards mitigating the class-imbalance problem for partial label learning[C] //Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York, USA: Association for Computing Machinery, 2018: 2427-2436. [24] 周瑜,顾宏.面向不平衡数据的逻辑回归偏标记学习算法[J].大连理工大学学报,2017,57(2):184-188. ZHOU Yu, GU Hong. Logistic regression biased labeling learning algorithm for imbalanced data[J]. Journal of Dalian University of Technology, 2017, 57(2):184-188. [25] HASTIE T, TIBSHIRANI R, FRIEDMAN J. The elements of statistical learning[J]. Journal of the Royal Statistical Society, 2004, 167(1):192. [26] HAN H, WANG W Y, MAO B H. Borderline-smote: a new over-sampling method in imbalanced data sets learning[C] //Proceedings of the 2005 International Conference on Advances in Intelligent Computing: Volume Part I(ICIC'05). Berlin, Germany: Springer-Verlag, 2005: 878-887. [27] JING P, HEISTERKAMP D R, DAI H K. Adaptive quasiconformal kernel nearest neighbor classification[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2004, 26(5):656-661. [28] PANIS G, LANITIS A. An overview of research activities in facial age estimation using the FG-NET aging database[C] //Proceedings of Lecture Notes in Computer Science(Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).Zurich, Switzerland: Lecture Notes in Computer Science, 2015, 8926: 737-750. [29] BRIGGS F, FERN X Z, RAICH R. Rank-loss support instance machines for MIML instance annotation[C] //Proceedings of the ACM SIGKDD International Con-ference on Knowledge Discovery and Data Mining. New York, USA: Association for Computing Machinery, 2012: 534-542. |
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