Amari S, Wu S. Improving support vector machine classifiers by modifying kernel functions [J]. Neural Networks, 1999,12 (6) : 783-789.
[2]
Kwok James T, Tsang Ivor W. Learning with idealized kemels[ A ]. In: Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003) [C], Washington, DC, USA, 2003:1233-1237.
[3]
Xing E, Ng A, Jordan M, et al. Distance metric learning, with application to clustering with side-information [ A ]. In : Advances in Neural Information Processing System [C] , Cambridge, MA, USA, 2002 : 1 - 9.
[4]
Graf Arnulf B A, Smola Alexander J, Borer S. Classification in a normalized feature space using support vector machines [J]. IEEE Transactions on Neural Networks, 2003, 14(3 ) :597 - 605.
[5]
Lin Y, Lee Y, Wahba G. Support vector machines for classification in nonstandard situations [J]. Machine Learning, 2002, 46(3):191 -202.
[6]
Chiang J H, Hao P Y. A new kernel based fuzzy clustering approach : support vector clustering with cell growing [J]. IEEE Transactions on Fuzzy Systems, 2003, 11 ( 4 ) :518 - 527.
Chen Y, Wang J Z. Support vector learning for fuzzy rule based classifications system [J]. IEEE Transactions on Fuzzy Systems, 2003, 11 ( 6 ) :716-728.
[9]
Cristianini N, Shawe-Tayor J, Elisseeff A, et al. On kernel target alignment [ A ]. In: Advances in Neural Information Processing System [C], Cambridge, MA, USA, 2002:367-373.
[10]
Herbrich R, Graepel T. A PAC-bayesian margin bound for linear classifiers:why SVM\\'s work [J]. IEEE Transactions on Information Theory, 2002:48( 12): 3140-3150.
[11]
Vapnik V, Chapelle O. Bounds on error expectation for support vector machines [J]. Neural Computation, 2000, 12 (9) :585 - 592.
[12]
Scholkopf B, Smola A. Learning with kernels: support vector machines, regularization, optimization, and beyond [M]. Cambridge, MA,USA: MIT Press, 2002:12-45.
[13]
Wisconsin breast cancer [EB/OL]. http://www. ics. uci. edu/- relearn/ MLRepository. html.