Smola A J, Scholkopf B. A tutorial on support vector regression [J]. Statistics and Computing, 2004, 14 (3) : 199-222.
[2]
Song Q, Hu W, Xie W. Robust support vector machine with bullet hole image classification [J]. IEEE Transactions on Systems, Man and Cybernetics, 2002, 32(4):440-448.
[3]
Xu Lin-li, Crammer K, Schuurmans D. Robust support vector machine training via convex outlier ablation [ A ]. In : Proceedings of the 21 st National Conference on Artificial Intelligence [C] , Boston, Massachusetts, USA, 2006: 536-546.
[4]
Wang Shi-tong, Zhu Jia-gang, Chung Fu-Lai, et al. Experimental study on parameter choices in norm-r support vector regression machines with noisy input [J]. Soft Computing, 2006, 10(3): 219-223.
Zhu Jia-gang, Wang Shi-tong, Wu Xi-sheng, et al. A novel adaptive SVR based filter ASBF for image restoration [J]. Soft Computing, 2006, 10(8) : 665-672.
Otsu N. A threshold selection method from gray-level histograms [J]. IEEE Transactions on Systems, Man and Cybernetics, 1979, 9( 1 ) : 62-66.
[9]
Scholkopf B, Smola A J, Williamson R C, et al. New support vector algorithm [J]. Neural Computation, 2000, 12(12) : 1207-1245.
[10]
Weston J, Herbrich R. Adaptive margin support vector machines [ A]. In: Smola A J, Bartlett P, Sch~lkopf B, et al. eds: Advances in Large Margin Classifiers[C], Cambridge, MA, USA: MIT Press, 2000 : 281-295.
[11]
Zhan Yi-qiang, Shen Ding-gang. An adaptive error penalization method for training an efficient and generalized SVM [J] . Pattern Recognition, 2006, 39(3) : 342-350.
Suykens J A K, De Brahanter J, Lukas L, et al. Weighted least squares support vector machine: robustness and sparse approximation [J]. Neurocomputing, 2002, 48(1-4) : 85-105.
[14]
Chuang C C, Su F F, Jeng J T, et al. Robust support regression networks for function approximation with outliers [J] . IEEE Transactions on Neural Networks, 2002, 13 (6) : 1322-1330.
[15]
Zhan Yong, Cheng Hao-zhong. A robust support vector algorithm for harmonic and interharmonic analysis of electric power system [J].Electric Power Systems Research, 2005, 73 (3) : 393-400.
[16]
Wang Shi-tong, Zhu Jia-gang, Chung Fu-lai, et al. Theoretically optimal parameter choices for support vector regression machines with noisy input [J]. Soft Computing, 2005, 9(10) : 732-741.
[17]
Lin Tau-chao, Yu Pao-ta. Adaptive two-pass median filter on support vector machines for image restoration [J] . Neural Computation, 2004, 16(2): 333-354.
[18]
Dunn J C. A fuzzy relative of the ISODATA process and its use in detecting compact, well-separated clusters [J] . Journal of Cybernetics, 3(3) :32-57, 1973.
[19]
王宗明 仇性启.奥里乳化油燃烧器设计与实验[J].石油化工设备,2002,31(1):23-25.
[20]
Tizhoosh H R. Image thresholding using type Ⅱ fuzzy sets [J]. Pattern Recognition, 2005, 38( 12): 2363-2372.
