[1]Moya M, Koch M, Hostetler L. One-class classifier networks for target recognition applications[C]// World Congress on Neural Networks. 1993:797-801.
[2]潘志松,陈斌,缪志敏,等. One Class分类器研究[J]. 电子学报, 2009,37(11):2496-2503.
[3]Tax D M J. One-class Classification: Concept-learning in the Absence of Counter-examples[D]. Netherlands:Delft University of Technology, 2001.
[4]Yan Yuesong, Wang Qiong, Ni Guiqiang, et al. One-class support vector machines based on matrix patterns[C]// Proceedings of the 2011 International Conference on Informatics, Cybernetics, and Computer Engineering. 2012,111:223-231.
[5]Kang I, Jeong M K, Kong D. A differentiated one-class classification method with applications to intrusion detection[J]. Expert Systems with Applications, 2012,39(4):3899-3905.
[6]Cyganek B. One-class support vector ensembles for image segmentation and classification[J]. Journal of Mathematical Imaging and Vision, 2012,42(2-3):103-117.
[7]Wang Chengqun, Lu Jiangang, Hu Chonghai, et al. Kernel matrix learning for one-class classification[J]. Advances in Neural Networks, 2008,5263:753-761.
[8]Labusch K, Timm F, Martinetz T. Simple incremental one-class support vector classification[J]. Lecture Notes in Computer Science, 2008,5096:21-30.
[9]Camci F, Chinnam R B. General support vector representation machine for one-class classification of non-stationary classes [J]. Pattern Recognition: The Journal of the Pattern Recognition Society, 2008,41(10):3021-3034.
[10]Moya M M, Hush D R. Network constraints and multi-objective optimization for one-class classification[J]. Neural Networks, 1996,9(3):463-474.
[11]Bishop C M, Hinton G, Hinton G E. Neural Networks for Pattern Recognition[M]. Oxford University Press, 1995.
[12]Japkowicz N. Concept-learning in the Absence of Counter-examples: An Autoassociation-based Approach to Classification[D]. The State University of New Jersey, 1999.
[13]Ypma A, Duin R P W. Novelty detection using self-organizing maps[C]// Proc. of ICONIP’97. 1997:1322-1325.
[14]Hempstalk K, Frank E, Witten I H. One-class classification by combining density and class probability estimation[J]. Lecture Notes in Computer Science, 2008,5211:505-519.
[15]Angiulli F. Prototype-based domain description for one-class classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012,34(6):1131-1144.
[16]Angiulli F. Condensed nearest neighbor data domain description[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007,29(10):1746-1758.
[17]Yeung D Y, Chow C. Parzen-window network intrusion detectors[C]// Proceedings of the 16th International Conference on Pattern Recognition. 1994:385-388.
[18]Lipka N, Stein B, Anderka M. Cluster-based one-class ensemble for classification problems in information retrieval[C]// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2012:1041-1042.
[19]Cabral G G, Oliveira A L I, Cahú C B G. Combining nearest neighbor data description and structural risk minimization for one-class classification[J]. Neural Computing & Applications, 2009,18(2):175-183.
[20]Soille P. Morphological Image Analysis: Principles and Applications[M]. 2nd ed. Berlin: Springer, 2003.
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