[1] Vapnik V N. The Nature of Statistical Learning Theory [M]. New York: Springer, 2000, 123—170.
[4] Xu P, Chan A K. An efficient algorithm on multi-class support vector machine model selection[C]. Procee-dings of the International Joint Conference on Neural Net-works 2003. Portland, IEEE, 2003: 3229—3232.
[5] Chapelle O, Vapnik V N, Bousquet O, et al. Choosing multiple parameters for support vector machines[J]. Machine Learning, 2002, 46 (1):131—159.
[6] Keerthi S S. Efficient tuning of SVM hyper parameters using radius/margin bound and iterative algorithms[J]. IEEE Trans. Neural Networks, 2002, 13 (5):1225—1229.
[7] Musicant D R, Kumar V, Ozgur A. Optimizing F-measure with support vector machines [C]. In the Sixteenth International Florida Artificial Intelligence Research Society Conference, St. Augustine, Florida, USA, AAAI Press, 2003: 356—360.
[8] Eitrich T, Lang B. Efficient optimization of support vector machine learning parameters for unbalanced datasets[J]. Journal of computational and applied mathematics, 2006, 196 (2):425—436.
[9] Morik K, Brockhausen P, Joachims T. Combining sta-tistical learning with a knowledge-based approach-a case study in intensive care monitoring [C]. In 16th Pro ceedings of the International Conference on Machine Learning. San Mateo, Canada: Morgan Kaufman Publishers, 1999, 268— 277.
[10] Bo L F, Wang L, Jiao L C. Multiple parameter selection for LS-SVM using smooth leave-one-out error[J]. Lecture notes in computer science. Berlin: Springer, 2005, 851— 856.
[12] R tsch G. Benchmarks data sets [Online].http://ida. first.fraunhofer.de/projects/bench/benchmarks.htm, 1999.
[13] Takuya I, Shigeo A. Fuzzy support vector machine for pattern classification [C]. Proceeding of International Joint Conference on Neural Networks, Washington, D.C, 2001, 1449—1454.