第3期 邓思宇，等：基于PageRank的主动学习算法 ·559· [4]ZHOU Xueyuan,BELKIN M.Semi-supervised [14]GILAD-BACHRACH R,NAVOT A,TISHBY N.Ker- learning[J]//ournal of the royal statistical society,2010, nel query by committee (KQBC)[R].Technical Report 172(2):530 2003-88,Leibniz Center,the Hebrew University,2003. [5]WANG Min,MIN Fan,ZHANG Zhiheng,et al.Active [15]CAI Deng,HE Xiaofei.Manifold adaptive experimental learning through density clustering[J].Expert systems with design for text categorization[J].IEEE transactions on applications,2017,85:305-317. knowledge and data engineering,2012,24(4):707-719. 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IEEE International Conference on Acoustics,Speech and [18]LIU Dun,LI Tianrui,LIANG Decui.Incorporating logist- Signal Processing.Shanghai,China,2016:5320-5324. ic regression to decision-theoretic rough sets for classific- [8]SUN Shujin,ZHONG Ping,XIAO H,et al.An MRF mod- ations[J].International journal of approximate reasoning, el-based active learning framework for the spectral-spatial 2014,55(1:197-210. classification of hyperspectral imagery[J].IEEE journal of 作者简介： selected topics in signal processing,2015,9(6):1074-1088. 邓思宇，女，1993年生，硕士研究 [9]YANG Yi,MA Zhigang,NIE Feiping,et al.Multi-class 生，主要研究方向为代价敏感学习、主 active learning by uncertainty sampling with diversity 动学习。 maximization[J.International journal of computer vision. 2015,113(2):113-127. [10]XIONG Sicheng,AZIMI J,FERN X Z.Active learning of constraints for semi-supervised clustering[J].IEEE trans- actions on knowledge and data engineering,2014,26(1): 刘福伦，男，1993年生，硕土研究 43-54. 生，主要研究方向为代价敏感学习、粗 [11]BLOODGOOD M.Support vector machine active learn- 糙集、主动学习。 ing algorithms with query-by-committee versus closest- to-hyperplane selection[C]//Proceedings of 2018 IEEE 12th International Conference on Semantic Computing. Laguna Hills,USA,2018:148-155. [12]BRIN SERGEY,PAGE Lawrence.The anatomy of a 黄雨婷，女，1996年生，主要研究 方向为推荐系统。 large-scale hypertextual web search engine [J].Computer networks and ISDN systems,1998,30(1/7):107-117. [13]DENG Zhenyun,ZHU Xiaoshu,CHENG Debo,et al.Ef- ficient kNN classification algorithm for big data[J]. Neurocomputing,2016,195:143-148.ZHOU Xueyuan, BELKIN M. Semi-supervised learning[J]//Journal of the royal statistical society, 2010, 172(2): 530. [4] WANG Min, MIN Fan, ZHANG Zhiheng, et al. Active learning through density clustering[J]. Expert systems with applications, 2017, 85: 305–317. [5] 胡小娟, 刘磊, 邱宁佳. 基于主动学习和否定选择的垃圾 邮件分类算法[J]. 电子学报, 2018, 46(1): 203–209. HU Xiaojuan, LIU Lei, QIU Ningjia. A novel spam cat￾egorization algorithm based on active learning method and negative selection algorithm[J]. Acta electronica sinica, 2018, 46(1): 203–209. [6] SYED A R, ROSENBERG A, KISLAL E. Supervised and unsupervised active learning for automatic speech recogni￾tion of low-resource languages[C]// Proceedings of 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China, 2016: 5320–5324. [7] SUN Shujin, ZHONG Ping, XIAO H, et al. An MRF mod￾el-based active learning framework for the spectral-spatial classification of hyperspectral imagery[J]. IEEE journal of selected topics in signal processing, 2015, 9(6): 1074–1088. [8] YANG Yi, MA Zhigang, NIE Feiping, et al. Multi-class active learning by uncertainty sampling with diversity maximization[J]. International journal of computer vision, 2015, 113(2): 113–127. [9] XIONG Sicheng, AZIMI J, FERN X Z. Active learning of constraints for semi-supervised clustering[J]. IEEE trans￾actions on knowledge and data engineering, 2014, 26(1): 43–54. [10] BLOODGOOD M. Support vector machine active learn￾ing algorithms with query-by-committee versus closest￾to-hyperplane selection[C]//Proceedings of 2018 IEEE 12th International Conference on Semantic Computing. Laguna Hills, USA, 2018: 148–155. [11] BRIN SERGEY, PAGE Lawrence. The anatomy of a large-scale hypertextual web search engine [J]. Computer networks and ISDN systems, 1998, 30(1/7): 107-117. [12] DENG Zhenyun, ZHU Xiaoshu, CHENG Debo, et al. Ef￾ficient kNN classification algorithm for big data[J]. Neurocomputing, 2016, 195: 143–148. [13] GILAD-BACHRACH R, NAVOT A, TISHBY N. Ker￾nel query by committee (KQBC)[R]. Technical Report 2003–88, Leibniz Center, the Hebrew University, 2003. [14] CAI Deng, HE Xiaofei. Manifold adaptive experimental design for text categorization[J]. IEEE transactions on knowledge and data engineering, 2012, 24(4): 707–719. [15] MIN Fan, ZHU W. A competition strategy to cost-sensit￾ive decision trees[C]//Proceedings of the 7th International Conference on Rough Sets and Knowledge Technology. Chengdu, China, 2012: 359–368. [16] 张桃, 吴小伟. 基于 PageRank 的马尔可夫链研究[J]. 电 子设计工程, 2017, 25(9): 36–38. ZHANG Tao, WU Xiaowei. The study of Markov chains based on PageRank[J]. Electronic design engineering, 2017, 25(9): 36–38. [17] LIU Dun, LI Tianrui, LIANG Decui. Incorporating logist￾ic regression to decision-theoretic rough sets for classific￾ations[J]. International journal of approximate reasoning, 2014, 55(1): 197–210. [18] 作者简介： 邓思宇，女，1993 年生，硕士研究 生，主要研究方向为代价敏感学习、主 动学习。 刘福伦，男，1993 年生，硕士研究 生，主要研究方向为代价敏感学习、粗 糙集、主动学习。 黄雨婷，女，1996 年生，主要研究 方向为推荐系统。 第 3 期 邓思宇，等：基于 PageRank 的主动学习算法 ·559·