Directional Greed Incremental SVDD Algorithm and Its Application

TONG Qi; HU Shuang-yan; YE Xia; ZHANG Zhong-min; LI Jun-shan

doi:10.3969/j.issn.1671-637x.2016.10.011

[1] TAX D M J, DUIN R P W.Support vector data description［J］.Machine Learning, 2004, 54(1):45-66.

[2] SHIN H J, EOM D H, KIM S S.One-class support vector machines-an application in machine fault detection and clssification［J］.Computers ＆ Industrial Engineering, 2005, 48(2):395-408.

[3] ZHUANG J F, LUO J, PENG Y Q, et al.On-line fault detection method based on modified SVDD for industrial process system［C］//IEEE Xplore, Intelligent System and Knowledge Engineering, 2008:567-572.

[4] XIE L, KRUGER U.Statistical processes monitoring based on improved ICA and SVDD［J］.Intelligent Computing, 2006, 4113(3):1247-1256.

[5] TAX D M J.One-class classification［D］.Netherlands:Technische Universiteit Delft, 2001.

[6] CHU C S, TSANG I W, KWOK J T.Scaling up support vector data descerption by using core-sets［C］//IEEE International Joint Conference on Neural Networks, 2004:425-430.

[8] TAX D M J, LASKOV P.Online SVM learning: from classification to data description and back［C］//Proceedings of IEEE International Workshop on Neural Networks for Signal Proceeding, 2003:499-508.

[9] CAUWENBERGHS G, POGGIO T.Incremental and decremental support vector machine learning［J］.Advances in Neural Information Processing Systems, 2001, 13(5):409-415.

[10] GALMEANU H, ANDONIE R.Implementation issues of an incremental and decremental SVM［C］//The 18th International Coference on Artificial Neural Networks, 2008:325-335.