Tanaka H, Uejima S, Asai K. Linear Regression Analysis with Fuzzy Model. IEEE Trans on Systems, Man, and Cybernetics, 1982, 12(6): 903907
[2]
Yen K K, Ghoshray S, Roig G. A Linear Regression Model Using Triangular Fuzzy Number Coefficients. Fuzzy Sets and Systems, 1999, 106(2): 167177
[3]
Gao J B, Gunn S R, Ham’s C J, et al. A Probabilistic Framework for SVM Regression and Error Bar Estimation. Machine Learning, 2002, 46(3): 7189
[4]
Kwok J T, Tsang I W. Linear Dependency between ε and the Input Noise in εSupport Vector Regression. IEEE Trans on Neural Networks, 2003, 14(3): 544553
[5]
Wang Shitong, Zhu Jiagang, Chung F L, et al. TheoreticallyOptimal Parameter Choices for Support Vector Regression Machines with Noisy Input. Soft Computing, 2005, 9(10): 732741
[6]
Wu Jinpei. Device Fault Diagnosis Using Possibility Theory. Fuzzy Systems and Mathematics, 1999, 13(2): 2532 (in Chinese) (吴今培.基于可能性理论的设备故障诊断.模糊系统与数学, 1999, 13(2): 2532)
[7]
Law M H, Kwok J T. Bayesian Support Vector Regression // Proc of the 8th International Workshop on Artificial Intelligence and Statistics. Key West, USA, 2001: 239244
[8]
Wang Shitong, Chung K F, Shen Hongbin, et al. Note on the Relationship between Probabilistic Fuzzy Clustering. Soft Computing, 2004, 8(7): 523526
[9]
Hong D H, Hwang C. Support Vector Fuzzy Regression Machines. Fuzzy Sets and Systems, 2003, 138(3): 271281
