Dependency between degree of fit and input noise in fuzzy linear regression using non-symmetric fuzzy triangular coefficients

Fuzzy Sets and Systems

Volumn 158, Issue 19, 2007, Pages 2189-2202

  Ge, Hong Wei ^a   Wang, Shi Tong ^a  

^a JIANGNAN UNIVERSITY   (China)

Author keywords

Fuzzy linear regression model; Fuzzy regression analysis; MAP; Optimal parameter choice; Possibility theory

Indexed keywords

FUZZY LOGIC; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PROBLEM SOLVING;

FUZZY LINEAR REGRESSION MODEL; FUZZY REGRESSION ANALYSIS; OPTIMAL PARAMETER CHOICE; POSSIBILITY THEORY;

LINEAR REGRESSION;

EID: 34547559857     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2007.03.011     Document Type: Article

References (17)

1. Gao J.B., Gunn S.R., and Ham's C.J. A probabilistic framework for SVM regression and error bar estimation. Mach. Learning 46 (2002) 71-89
2. Hong D.H., and Hwang C. Support vector fuzzy regression machines. Fuzzy Sets and Systems 138 (2003) 271-281
3. Kwok J.T., and Tsang I.W. Linear dependency between ε{lunate} and the input noise in ε{lunate}-support vector regression. IEEE Trans. Neural Networks 14 3 (2003) 544-553
4. M.H. Law, J.T. Kwok, Bayesian support vector regression, in: Proc. English Internat. Workshop on Artificial Intelligence and Statistics, Florida, 2001, pp. 239-244.
5. Peters G. Fuzzy linear regression with fuzzy intervals. Fuzzy Sets and Systems 63 (1994) 45-55
6. Redden D.T., and Woodall W.H. Properties of certain fuzzy linear regression methods. Fuzzy Sets and Systems 64 (1994) 361-375
7. Sakawa M., and Yano H. Multiobjective fuzzy linear regression analysis for fuzzy input-output data. Fuzzy Sets and Systems 47 (1992) 173-181
8. Savic D., and Pedrycz W. Evaluation of fuzzy linear regression models. Fuzzy Sets and Systems 39 (1991) 51-63
9. Tanaka H. Fuzzy data analysis by possibilistic linear model. Fuzzy Sets and Systems 24 (1987) 363-375
10. Tanaka H. Possibilistic linear system and their application to the linear regression model. Fuzzy Sets and Systems 27 (1988) 275-289
11. Tanaka H., and Ishibuchi H. Identification of possibilistic linear systems by quadratic membership functions of fuzzy parameters. Fuzzy Sets and Systems 41 (1991) 145-160
12. Tanaka H., and Ishibuchi H. Possibilistic regression analysis based on linear programming. In: Kacprzyk J., and Fedrizzi M. (Eds). Fuzzy Regression Analysis (1992), Heidelberg, Germany, Omnitech, Warsaw, Poland, Springer 47-60
13. Tanaka H., Ishibuchi H., and Yoshikawa S. Exponential possibility regression analysis. Fuzzy Sets and Systems 69 (1995) 305-318
14. Tanaka H., and Lee H. Interval regression analysis by quadratic programming approach. IEEE Trans. Fuzzy Systems 6 4 (1998) 473-481
15. Tanaka H., Uejima S., and Asai K. Linear regression analysis with fuzzy model. IEEE Trans. Systems Man Cybernet. 12 6 (1982) 903-907
16. Wang S. Theoretically optimal parameter choices for support vector regression machines with noisy input. Internat. J. Soft Comput. 9 10 (2005) 732-741
17. Yen K.K., Ghoshray S., and Roig G. A linear regression model using triangular fuzzy number coefficients. Fuzzy Sets and Systems 106 (1999) 167-177