[1] GOUTTE C, CANCEDDA N, DYMETMAN M, et al. Semi-supervised learning for machine translation[J]. Journal of the Royal Statistical Society, 2017, 172(2):530-530. [2] ZHU S, SUN X, JIN D. Multi-view semi-supervised learning for image classification[J]. Neurocomputing, 2016, 208(10):136-142. [3] XU C, TAO D, XU C. Large-margin multi-view information bottleneck[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(8):1559-1572. [4] DU J, LING C X, ZHOU Z H. When does cotraining work in real data?[J]. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(5):788-799. [5] QIAN T, LIU B, CHEN L, et al. Tri-training for authorship attribution with limited training data:a comprehensive study[J]. Neurocomputing, 2016, 171(1):798-806. [6] DEKEL O, GENTILE C, SRIDHARAN K. Selective sampling and active learning from single and multiple teachers[J]. Journal of Machine Learning Research, 2016, 13(1):2655-2697. [7] SENER O, SAVARESE S. Active learning for convolutional neural networks:a core-set approach[J]. arXiv E-print, 2017:arXiv:1708.00489. [8] PIROONSUP N, SINRHUPINVO S. Analysis of training data using clustering to improve semi-supervised self-training[J]. Knowledge-Based Systems, 2018, 143(2):65-80. [9] WANG X Z, ASHFAG R A R, FU A M. Fuzziness based sample categorization for classifier performance improvement[J]. Journal of Intelligent and Fuzzy Systems, 2015, 29(3):1185-1196. [10] ZHANG Y, WEN J, WANG X, et al. Semi-supervised learning combining co-training with active learning[J]. Expert Systems with Applications, 2014, 41(5):2372-2378. [11] GAN H, SANG N, HUANG R, et al. Using clustering analysis to improve semi-supervised classification[J]. Neurocomputing, 2013, 25(3):290-298. [12] 龚彦鹭,吕佳.结合半监督聚类和加权KNN的协同训练方法[J/OL].计算机工程与应用,2019:1-9[2018-12-28]. http://kns.cnki.net/kcms/detail/11.2127.TP.20181218.1748.032.html. (GONG Y L, LYU J. Co-training method combined semi-supervised clustering and weighted K nearest neighbor[J/OL]. Computer Engineering and Applications,2019:1-9[2018-12-28]. http://kns.cnki.net/kcms/detail/11.2127.TP.20181218.1748.032.html.) [13] RODRIGUEZ A, LAIO A. Clustering by fast search and find of density peaks[J]. Science, 2014, 344(6191):1492-1496. [14] WU D, SHANG M S, LUO X, et al. Self-training semi-supervised classification based on density peaks of data[J]. Neurocomputing, 2018, 275(1):180-191. [15] 罗云松,吕佳.结合密度峰值优化模糊聚类的自训练方法[J].重庆师范大学学报(自然科学版),2019,36(2):74-80. (LUO Y S, LYU J. Self-training algorithm combined with density peak optimization fuzzy clustering[J]. Journal of Chongqing Normal University (Natural Science), 2019, 36(2):74-80.) |