David M J T, Robert P W D. Support vector data description. Machine Learning, 2004, 54(1): 45-66
[3]
Babich G A, Camps O I. Weighted Parzen windows for pattern classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(5): 567-570
[4]
Tsang I W, Kwok J T, Cheung P M. Core vector machines: fast SVM training on very large data sets. Journal of Machine Learning Research, 2005, 6: 363-392
[5]
Tsang I W H, Kwok J T Y, Zurada J A. Generalized core vector machines. IEEE Transactions on Neural Networks, 2006, 17(5): 1126-1140
[6]
Li M, Kwok J T, Lu B L. Making large-scale Nystr？m approximation possible. In: Proceedings of the 27th International Conference on Machine Learning. Haifa, Isreal, 2010. 631-638
[7]
Fine S, Scheinberg K. Efficient SVM training using low-rank kernel representations. Journal of Machine Learning Research, 2001, 2: 243-264
[8]
Peng Xin-Jun, Wang Yi-Fei. Total margin ν-support vector machine and its geometric problem. Pattern Recognition and Artificial Intelligence, 2009, 22(1): 8-16 (彭新俊, 王翼飞. 总间隔ν-!支持向量机及其几何问题. 模式识别与人工智能, 2009, 22(1): 8-16)
[9]
Donoho D L, Grimes C. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proceedings of the National Academy of Sciences of the United States of America, 2005, 100(10): 5591-5596
[10]
Wu M R, Ye J P. A small sphere and large margin approach for novelty detection using training data with outliers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(11): 2088-2092
[11]
Tao J W, Wang S T, Hu W J, Ying W B. ρ-margin kernel learning machine with magnetic field effect for both binary classification and novelty detection. International Journal of Software and Informatics, 2010, 4(3): 305-324
[12]
Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300
[13]
De Brabanter K, Karsmakers P, Ojeda F, Alzate C, De Brabanter J, Pelckmans K, De Moor B, Vandewalle J, Suykens J A K. LS-SVMlab Toolbox User's Guide Version 1.8 [Online], available: http://www.esat.kuleuven.be/sista/lssvmlab/, October 30, 2011
[14]
Qian Peng-Jiang, Wang Shi-Tong, Deng Zhao-Hong. Fast adaptive similarity-based clustering using sparse Parzen window density estimation. Acta Automatica Sinica, 2011, 37(2): 179-187 (钱鹏江, 王士同, 邓赵红. 基于稀疏Parzen窗密度估计的快速自适应相似度聚类方法. 自动化学报, 2011, 37(2): 179-187)
[15]
Deng Z H, Chung F L, Wang S T. FRSDE: fast reduced set density estimator using minimal enclosing ball approximation. Pattern Recognition, 2008, 41(4): 1363-1372
[16]
Chung F L, Deng Z H, Wang S T. From minimum enclosing ball to fast fuzzy inference system training on large datasets. IEEE Transactions on Fuzzy Systems, 2009, 17(1): 173-184
[17]
Williams C, Seeger M. Using the Nystr？m method to speed up kernel machines. Advances in Neural Information Processing Systems, 2001, 13: 682-688
[18]
Achlioptas D, McSherry F, Sch？lkopf B. Sampling techniques for kernel methods. Advances in Neural Information Processing Systems, 2001, 14: 335-342
[19]
Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding. Science, 2000, 290(5500): 2323-2326
[20]
Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 2003, 15(6): 1373-1396
[21]
Kubat M, Matwin S. Addressing the curse of imbalanced training sets: one-sided selection. In: Proceedings of the 14th International Conference on Machine Learning. Nashville: Morgan Kaufmann Publishers, 1997. 179-186
[22]
Suykens J A K, Van Gestel T, De Brabanter J, De Moor B, Vandewalle J. Least Squares Support Vector Machines. Singapore: World Scientific, 2002. 173-182
[23]
Hull J J. A database for handwritten text recognition research. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(5): 550-554
