Vapnik V N. The Nature of Statistical Learning Theory. New York, USA: SpringerVerlag, 1995
[2]
Nguyen H N, Ohn S Y. Unified Kernel Function and Its Training Method for SVM // Proc of the 13th International Conference on Neural Information Processing. Hongkong, China, 2006: 792800
[3]
Vapnik V N, Golowich S E, Smola A. Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing // Mozer M C, Jordan M I, Petsche T, eds. Advances in Neural Information Processing Systems. London, UK: MIT Press, 1997,9: 281287
[4]
Li Qing, Jiao Licheng, Hao Yingjuan. Adaptive Simplification of Solution for Support Vector Machine. Pattern Recognition, 2007, 40(3): 972980
[5]
Smola A J, Schlkopf B. A Tutorial on Support Vector Regression. Statics and Computing, 2004, 14(3): 199222
[6]
Suykens J A K, Vandewalle J. Least Squares Support Vector Machines Classifiers. Neural Processing Letters, 1999, 9(3): 293300
[7]
Suykens J A K, Gestel T V, Brabanter J D, et al. Least Squares Support Vector Machines. Singapore, Singapore: World Scientific, 2002
[8]
An S J, Liu W Q, Venkatesh S. Fast CrossValidation Algorithms for Least Squares Support Vector Machine and Kernel Ridge Regression. Pattern Recognition, 2007, 40(8): 21542162
[9]
Suykens J A K, de Brabanter J, Lukas L, et al. Weighted Least Squares Support Vector Machines: Robustness and Sparse Approximation. Neurocomputing, 2002, 48(1): 85105
[10]
Wen Wen, Hao Zhifeng, Shao Zhuangfeng, et al. A Heuristic WeightSetting Algorithm for Robust Weighted Least Squares Support Vector Regression // Proc of the 13th International Conference on Neural Information Processing. Hongkong, China, 2006: 773781
[11]
Yan Weiwu, Chang Junlin, Shao Huihe. Least Squares SVM Regression Method Based on Sliding Time Window and Its Simulation. Journal of Shanghai Jiaotong University, 2004, 38(4): 524526 (in Chinese) (阎威武,常俊林,邵惠鹤.基于滚动时间窗的最小二积支持向量机回归估计方法及传真.上海交通大学学报, 2004, 38(4): 524526)
[12]
Suykens J A K, Lukas L, Vandewalle J. Sparse Approximation Using Least Squares Support Vector // Proc of the IEEE International Symposium on Circuits and Systems. Geneva, Switzerland, 2000, Ⅱ: 757760
[13]
Baudat G, Anouar F. KernelBased Methods and Function Approximation // Proc of the International Joint Conference on Neural Networks. Washington, USA, 2001, Ⅱ: 12441249
[14]
Hang Hongxuan, Han Jiye. Mathematical Programming. Beijing, China: Tsinghua University Press, 2006 (in Chinese) (黄红选,韩继业. 数学规划. 北京:清华大学出版社, 2006)
