Vapnik V N. The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995
[2]
Vapnik V N. An Overview of Statistical Learning Theory. IEEE Trans on Neural Networks, 1999, 10(5): 988-999
[3]
Mangasarian O L. Generalized Support Vector Machines. In: Smola A, Bartlett P, Schlkopf B, Schuurmans D, eds. Advances in Large Margin Classifiers. Cambridge, USA: MIT Press, 2000, 135-146
[4]
Lee Y J, Mangasarian O L. SSVM: A Smooth Support Vector Machine. Computational Optimization and Applications, 2001, 20(1): 5-22
[5]
Joachims T. Making Large-Scale SVM Learning Practical. In: Schlkopf B, et al, eds. Advances in Kernel Method-Support Vector Learning, Cambridge, USA: MIT Press, 1999, 169-184
[6]
Mangasarian O L, Musicant D R. Active Set Support Vector Machine Classification. In: Lee T K, Dietterich T G, Tresp V, eds. Neural Information Processing Systems. Cambridge, USA: MIT Press, 2001, 577-583
[7]
Mangasarian O L, Musicant D R. Lagrangian Support Vector Machines. Journal of Machine Learning Research, 2001, 1(3): 161-177
[8]
Lin C J, Saigal R. An Incomplete Cholesky Factorization for Dense Matrices. BIT, 2000, 40(13): 536-558
[9]
Zhang Y. Solving Large-Scale Linear Programs by Interior-Point Methods under the MATLAB Environment. Optimization Methods and Software, 1998, 10(1): 1-31
[10]
Joachims T. SVMlight.1998. http://svmlight.joachims.org/
[11]
Platt J. Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Schlkopf B, et al. eds. Advances in Kernel Method-Support Vector Learning. Cambridge, USA: MIT Press, 1999, 185-208
[12]
Mangasarian O L, Musicant D R. Successive Overrelaxation for Support Vector Machines. IEEE Trans on Neural Networks, 1999,10(5): 1032-1037
[13]
Zhou S S, Rong X F, Zhou L H. A Maximum Entropy Method for Training the Support Vector Machines. Signal Processing, 2003, 19(6): 595-599 (in Chinese) (周水生, 容晓锋, 周利华. 训练支撑向量机的极大熵方法. 信号处理, 2003, 19(6): 595-599)
[14]
Zhou S S, Zhou L H. Lower Dimension Newton- Algorithm for Training the Support Vector Machines. Systems Engineering and Electronics, 2004, 26(9): 1315-1318 (in Chinese) (周水生, 周利华. 训练支撑向量机的低维Newton算法. 系统工程与电子技术, 2004, 26(9): 1315-1318)
[15]
Mangasarian O L. Nonlinear Programming. New York, USA: McGraw-Hill, 1969
[16]
Bazaraa M S, Sherali H D, Shetty C M. Nonlinear Programming, Theory and Algorithms. New York, USA: John Wiley and Sons, 1993
[17]
Golub G H, van Loan C F. Matrix Computations. 3rd Edition. Baltimore, USA: The John Hopkins University Press, 1996
[18]
Lee Y J, Mangasarian O L. RSVM: Reduced Support Vector Machines. In: Proc of the 1st SIAM International Conference on Data Mining. Chicago, USA, 2001. ftp://ftp.cs.wisc.edu/pub/dmi/techreports/00-07.ps
[19]
Yuan Y X, Sun W Y. Optimization Theory and Method . Beijing, China: Science Press, 2003 (in Chinese) (袁亚湘, 孙文瑜. 最优化理论与方法. 北京: 科学出版社, 2003)
