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Mingrui Wu, Jieping Ye, "A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 11, pp. 20882092, November, 2009.  
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@article{ 10.1109/TPAMI.2009.24, author = {Mingrui Wu and Jieping Ye}, title = {A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers}, journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {31}, number = {11}, issn = {01628828}, year = {2009}, pages = {20882092}, doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2009.24}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Pattern Analysis and Machine Intelligence TI  A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers IS  11 SN  01628828 SP2088 EP2092 EPD  20882092 A1  Mingrui Wu, A1  Jieping Ye, PY  2009 KW  Novelty detection KW  oneclass classification KW  support vector machine KW  kernel methods. VL  31 JA  IEEE Transactions on Pattern Analysis and Machine Intelligence ER   
[1] C. Campbell and K.P. Bennett, “A Linear Programming Approach to Novelty Detection,” Y. Weiss, B. Schölkopf, and J. Platt, eds., Advances in Neural Information Processing Systems 12, MIT Press, 2000.
[2] L.J. Cao, H.P. Lee, and W.K. Chong, “Modified Support Vector Novelty Detector Using Training Data with Outliers,” Pattern Recognition Letters, vol. 24, pp. 24792487, 2003.
[3] C.C. Chang and C.J. Lin, “LIBSVM: A Library for Support Vector Machines,” the LIBSVM software package is available at http://www.csie.ntu.edu.tw/~cjlinlibsvm, 2001.
[4] C.C. Chang and C.J. Lin, “Training $\nu$ Support Vector Classifiers: Theory and Algorithms,” Neural Computation, vol. 14, pp. 19591977, 2002.
[5] O. Chapelle, V. Vapnik, O. Bousquet, and S. Mukherjee, “Choosing Multiple Parameters for Support Vector Machines,” Machine Learning, vol. 46, nos. 13, pp. 131159, 2002.
[6] N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer, “SMOTE: Synthetic Minority OverSampling Technique,” J. Artificial Intelligence Research, vol. 16, pp. 321357, 2002.
[7] W. Karush, “Minima of Functions of Several Variables with Inequalities as Side Constraints,” master's thesis, Dept. of Math., Univ. of Chicago, 1939.
[8] M. Kubat and S. Matwin, “Addressing the Curse of Imbalanced Training Sets: OneSided Selection,” Proc. 14th Int'l Conf. Machine Learning, 1997.
[9] H.W. Kuhn and A.W. Tucker, “Nonlinear Programming,” Proc. Second Berkeley Symp. Math. Statistics and Probabilistics, pp. 481492, 1951.
[10] Y. Lin, Y. Lee, and G. Wahba, “Support Vector Machine for Classification in Nonstandard Situations,” Machine Learning, vol. 46, pp. 191202, 2002.
[11] P.B. Nair, A. Choudhury, and A.J. Keane, “Bayesian Framework for Least Squares Support Vector Machine Classifiers, Gaussian Processes and Kernel Fisher Discriminant Analysis,” Neural Computation, vol. 15, pp.11151148, 2002.
[12] E. Pekalska, D.M. Tax, and R.P.W. Duin, “OneClass LP Classifier for Dissimilarity Representations,” Advances in Neural Information Processing Systems 12, S. Thrun, S. Becker, and K. Obermayer, eds., MIT Press, 2003.
[13] S. Roberts and L. Tarassenko, “A Probabilistic Resource Allocation Network for Novelty Detection,” Neural Computation, vol. 6, pp. 270284, 1994.
[14] B. Schölkopf and A.J. Smola, Learning with Kernels. MIT Press, 2002.
[15] C.D. Scott and R.D. Nowak, “Learning Minimum Volume Sets,” J. Machine Learning Research, vol. 7, pp. 665704, 2006.
[16] I. Steinwart, D. Hush, and C. Scovel, “A Classification Framework for Anomaly Detection,” J. Machine Learning Research, vol. 6, pp. 211232, 2005.
[17] J.A.K. Suykens, T.V. Gestel, J.D. Brabanter, B.D. Moor, and J. Vandewalle, Least Squares Support Vector Machines. World Scientific, 2002.
[18] D.M.J. Tax and R.P.W. Duin, “Support Vector Data Description,” Machine Learning, vol. 54, pp. 4566, 2004.
[19] G.G. Towel, “Local Expert Autoassociators for Anomaly Detection,” Proc. 17th Int'l Conf. Machine Learning, 2000.
[20] V. Vapnik, The Nature of Statistical Learning Theory. Springer Verlag, 1995.
[21] V. Vapnik and O. Chapelle, “Bounds on Error Expectation for Support Vector Machines,” Neural Computation, vol. 12, 2000.
[22] K. Veropoulos, C. Campbell, and N. Cristianini, “Controlling the Sensitivity of Support Vector Machines,” Proc. 16th Int'l Conf. Artificial Intelligence and Statistics, 1999.
[23] R. Vert and J.P. Vert, “Consistency and Convergence Rates of OneClass SVM and Related Algorithms,” J. Machine Learning Research, vol. 7, pp. 817854, 2006.
[24] G. Wu and E.Y. Chang, “ClassBoundary Alignment for Imbalanced Dataset Learning,” Proc. Int'l Conf. Machine Learning Workshop Learning from Imbalanced Datasets, 2003.