Vapnik V N. The Nature of Statistical Learning Theory. New York, USA: Springer-Verlag, 1995
[2]
Vapnik V N. Statistical Learning Theory. New York, USA: Wiley, 1998
[3]
Christianini V, Shawe-Taylor J. An Introduction to Support Vector Machines. Cambridge, UK: Cambridge University Press, 2002
[4]
Ripley B D. Pattern Recognition and Neural Networks. Cambridge, UK: Cambridge University Press, 1996
[5]
Jeng J T, Chuang C C, Su S F. Support Vector Interval Regression Networks for Interval Regression Analysis. Fuzzy Sets and Systems, 2003, 138(2): 283-300
[6]
Vapnik V N, Chapelle O. Bounds on Error Expectation for Support Vector Machines. Neural Computation, 2000, 12(9): 2013-2036
[7]
Chapelle O, Vapnik V N, Bousquet O, et al. Choosing Multiple Parameters for Support Vector Machines. Machine Learning, 2002, 46(1/2/3): 131-159
[8]
Schlkopf B, Smola A J, Williamson R C, et al. New Support Vector Machines. Neural Computation, 2000, 12(6): 1207-1245
[9]
Yoon M, Yun Y, Nakayama H. A Role of Total Margin in Support Vector Machines // Proc of the International Joint Conference on Neural Network. Portland, USA, 2003, Ⅲ: 2049-2053
[10]
Crisp D J, Burges C J C. A Geometric Interpretation of Nu-SVM Classifiers // Solla S A, Leen T K, Müller K R, eds. Advances in Neural Information. Cambridge, USA: MIT Press, 1999, 12: 244-250
[11]
Mavroforakis M E, Theodoridis S. A Geometric Approach to Support Vector Machine (SVM) Classification. IEEE Trans on Neural Network, 2007, 17(3): 671-682
[12]
Tao Qing, Sun Demin, Fan Jingsong, et al. Maximal Margin Linear Classifier Based on the Contraction of the Closed Convex Hull. Journal of Software, 2002, 13(3): 404-409 (in Chinese) (陶 卿,孙德敏,范劲松,等.基于闭凸包收缩的最大边缘线性分类器.软件学报, 2002, 13(3): 404-409)
