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李延超  等:自适应主动半监督学习方法                                                              3821


         [15]    Nguyen HT, Smeulders A. Active learning using pre-clustering. In: Proc. of the Int’l Conf. on Machine Learning. 2004. 79−88.
         [16]    Urner  R,  Wulff S, Ben-David S. Plal:  Cluster-based  active learning. In:  Proc. of  the  Annual Conf. on  Learning Theory. 2013.
             376−397.
         [17]    Calma A, Reitmaier T,  Sick B.  Semi-Supervised active  learning  for  support  vector machines: A  novel approach  that exploits
             structure information in data. Information Sciences, 2018,456:13−33.
         [18]    Haeusser P, Mordvintsev A, Cremers D. Learning by association-a versatile semi-supervised training method for neural networks.
             In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition. 2017. 172−181.
         [19]    Zhou ZH. Disagreement-based semi-supervised  learning. Acta Automatica  Sinica,  2013,39(11):1871−1878 (in Chinese with
             English abstract).
         [20]    Kendall A, Gal Y, Cipolla R. Multi-Task learning using uncertainty to weigh losses for scene geometry and semantics. In: Proc. of
             the IEEE Conf. on Computer Vision and Pattern Recognition. 2018. 7482−7491.
         [21]    Wei K, Iyer R, Bilmes  J.  Submodularity  in  data  subset selection and active learning.  In: Proc.  of the  Int’l Conf.  on Machine
             Learning. 2015. 1954−1963.
         [22]    Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep
             learning results. In: Proc. of the Advances in Neural Information Processing Systems. 2017. 1195−1204.
         [23]    Cai W, Zhang Y, Zhang Y, Zhou S, Wang W, Chen Z, Ding C. Active learning for classification with maximum model change.
             ACM Trans. on Information Systems, 2017,36(2):1−15.
         [24]    Bilenko M, Basu S, Mooney RJ. Integrating constraints and metric learning in semi-supervised clustering. In: Proc. of the Int’l
             Conf. on Machine Learning. 2004. 11−19.
         [25]    Mallapragada PK, Jin  R,  Jain  AK.  Active query selection for semi-supervised  clustering.  In: Proc.  of the  IEEE Int’l  Conf. on
             Pattern Recognition. 2008. 1−4.
         [26]    Xiong S, Azimi J, Fern XZ. Active learning of constraints for semi-supervised clustering. IEEE Trans. on Knowledge and Data
             Engineering, 2014,26(1):43−54.
         [27]    Xiong C, Johnson DM, Corso JJ. Active clustering with model-based uncertainty reduction. IEEE Trans. on Pattern Analysis and
             Machine Intelligence, 2017,39(1):5−17.
         [28]    Hoi SC, Jin R, Zhu J, Lyu MR. Batch mode active learning and its application to medical image classification. In: Proc. of the Int’l
             Conf. on Machine Learning. 2006. 417−424.
         [29]    Huang A, Milne D, Frank E, Witten IH. Clustering documents with active learning using Wikipedia. In: Proc. of the Int’l Conf.
             onData Mining. 2008. 839−844.
         [30]    Kutsuna N, Higaki T, Matsunaga  S, Otsuki T, Yamaguchi M,  Fujii H, Hasezawa  S. Active  learning  framework  with  iterative
             clustering for bioimage classification. Nature Communications, 2012,3:1032.
         [31]    Du B, Wang Z, Zhang L, Zhang L, Tao D. Robust and discriminative labeling for multi-label active learning based on maximum
             correntropy criterion. IEEE Trans. on Image Processing, 2017,26(4):1694−1707.
         [32]    Yang Y, Loog M. A variance maximization criterion for active learning. Pattern Recognition, 2018,78:358−370.
         [33]    Wang K, Zhang D, Li Y, Zhang R, Lin L. Cost-Effective active learning for deep image classification. IEEE Trans. on Circuits and
             Systems for Video Technology, 2017,27(12):2591−2600.
         [34]    Lin L, Wang K, Meng D, Zuo W, Zhang L. Active self-paced learning for cost-effective and progressive face identification. IEEE
             Trans. on Pattern Analysis and Machine Intelligence, 2018,40(1):7−19.
         [35]    Melendez J, van Ginneken B, Maduskar P, Philipsen RH, Ayles H, Sánchez CI. On combining multiple-instance learning and active
             learning for computer-aided detection of tuberculosis. IEEE Trans. onMedical Imaging, 2016,35(4):1013−1024.
         [36]    Wang Z,  Fang X, Tang X, Wu C. Multi-Class active  learning  by  integrating  uncertainty  and  diversity.  IEEE Access,  2018,22:
             794−803.
         [37]    Wang Z, Du B, Zhang L, Zhang L, Jia X. A novel semi-supervised active learning algorithm for hyperspectral image classification.
             IEEE Trans. on Geoscience and Remote Sensing, 2017,55(6):3071−3083.
         [38]    Basu S, Banerjee A, Mooney RJ. Active semi-supervision for pairwise constrained clustering. In: Proc. of the SIAM Int’l Conf. on
             Data Mining. 2004. 333−344.
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