(Tao X M, Hao S Y, Zhang D X, et al. Support vector machine for unbalanced data based on sample properties under-sampling approaches[J]. Control and Decision, 2013, 28(7): 978-984.)
(Zhou H R, Zheng P E, Ren X L. Method for selecting parameters of least squares support vector machines and application[J]. J of System Engineering, 2009, 24(2): 248-252.)
(Shang Z G, Yan H S. Forecasting product design time based on fuzzy support vector machine[J]. Control and Decision, 2012, 27(4): 531-534.)
[12]
Lee Y J, HsiehWF, Huang C M. SSVR: A smooth support vector machine for insensitive regression[J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(5): 678-685.
[13]
Gu J R, Zhu M C, Jiang L G Y. Housing price forecasting based on genetic algorithm and support vector machine[J]. Expert Systems with Applications, 2011, 38(4): 3383-3386.
[14]
Yanga B S, Hwanga W W, Kima D J, et al. Conditionclassification of small reciprocating compressor for refrigerators using artificial neural networks and support vector machines[J]. Mechanical Systems and Signal Processing, 2005, 19(2): 371-390.
(Yao X, Yu L A. A fuzzy proximal support vector machine model and its application to credit risk analysis[J]. Systems Engineering-Theory & Practice, 2012, 32(3): 549-554.)
[19]
Lin C F, Wang S D. Training algorithms for fuzzy support vector machines with noisy data[J]. Pattern Recognition Letters, 2004, 25(14): 1647-1656.
[20]
Li D F, Hu W C, Xiong W, et al. Fuzzy relevance vector machine for learning from unbalanced data and noise[J]. Pattern Recognition Letters, 2008, 29(9): 1175-1181.
(Zhang G X, Fei L, Du Z, et al. New noise-immune fuzzy SVM algorithm for unbalanced data[J]. Computer Engineering and Applications, 2008, 44(16): 142-144.)
[23]
Yang Z Z, Jin L J, Wang M H. Forecasting baltic panamax index with support vector machine[J]. J of Transportation Systems Engineerning & Information Technology, 2011, 11(3): 50-57.
[24]
Guo J, Zhou J Z, Qin H, et al. Monthly stream flow forecasting based on improved support vector machine model[J]. Expert Systems with Applications, 2011, 38(10): 13073-13081.
(Wu Q, Yan H S. Forecasting method based 0n support Vector m achine with Gaussian loss function[J]. Computer Integrated Manufacturing System, 2009, 15(2): 306-312.)
[29]
Li H X, Yang J L, Zhang G, et al. Probabilistic support vector machines for classification of noise affected data[J]. Information Sciences, 2013, 221(1): 60-71.
(Wu Q, Yan H S. Product sales forecasting model based on robust ??-support vector machine[J]. Computer Integrated anufacturing System, 2009, 15(6): 1081-1087.)
(Hu G S, Deng F Q. Support vector regression with piecewise loss function and its application in investment decision[J]. Control Theory & Applications, 2006, 23(2): 315-318.)
(Hu G S, Deng F Q. Multi-output support vector regression with piecewise loss function[J]. Control Theory & Applications, 2007, 24(5): 711-714.)
[36]
An W J, Liang M G. Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises[J]. Neurocomputing, 2013, 110(6): 101-110.
[37]
Yang X W, Tan L J, He L F. A robust least squares support vector machine for regression and classification with noise[J]. Neurocomputing, 2014, 140(9): 41-52.
[38]
Hu Q H, Zhang S G, Xie Z X, et al. Noise model based ??-support vector regression with its application to short-term wind speed forecasting[J]. Neural Networks, 2014, 57(9): 1-11.