Support vector interval regression machine for crisp input and output data

Fuzzy Sets and Systems

Volumn 157, Issue 8, 2006, Pages 1114-1125

  Hwang, Changha ^a   Hong, Dug Hun ^b   Ha Seok, Kyung ^c  

^a Dankook University   (South Korea)

^b Myong Ji University   (South Korea)

^c Inje University   (South Korea)

Author keywords

Interval regression analysis; Outliers; Possibility; Support vector regression

Indexed keywords

DATA REDUCTION; DATABASE SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING; ROBUSTNESS (CONTROL SYSTEMS);

INTERVAL REGRESSION ANALYSIS; OUTLIERS; POSSIBILITY; SUPPORT VECTOR REGRESSION;

REGRESSION ANALYSIS;

EID: 33644972832     PISSN: 01650114     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.fss.2005.09.008     Document Type: Article

References (22)

1. C.B. Cheng, and E.S. Lee Fuzzy regression with radial basis function network Fuzzy Sets and Systems 119 2001 291 301
2. D. Chwirut, Ultrasonic reference block study data, 1979, available at http://www.itl.nist.gov/div898/strd/nls/data/chwirut2.shtml.
3. B. Efron, and R.J. Tibshirani An Introduction to the Bootstrap 1993 Chapman & Hall London
4. W.R. Gilks, S. Richardson, and D.J. Spiegelhalter Markov Chain Monte Carlo in Practice 1996 Chapman & Hall London
5. S. Gunn, Support vector machines for classification and regression, ISIS Technical Report, University of Southampton, 1998.
6. F.R. Hampel, E.M. Ronchetti, P.J. Rousseeuw, and W.A. Stahel Robust Statistics: The Approach Based on Influence Functions 1986 Wiley New York
7. T. Hashiyama, T. Furuhash, Y. Uchikawa, An interval fuzzy model using a fuzzy neural network, Proc. Internat. Joint Conf. Neural Networks, Baltimore, MD, 1992, pp. 745-750.
8. L. Huang, B.L. Zhang, and Q. Huang Robust interval regression analysis using neural networks Fuzzy Sets and Systems 97 1998 337 347
9. H. Ishibuchi, and M. Nii Fuzzy regression using asymmetric fuzzy coefficients and fuzzified neural networks Fuzzy Sets and Systems 119 2001 200 273
10. H. Ishibuchi, H. Tanaka, Several formulation of interval regression analysis, Proc. Sino-Japan Joint Meeting on Fuzzy Sets and Systems, Beijing, China, 1990, pp. B2-2.
11. H. Ishibuchi, and H. Tanaka Fuzzy regression analysis using neural networks Fuzzy Sets and Systems 50 1992 57 65
12. H. Ishibuchi, H. Tanaka, and H. Okada An architecture of neural networks with interval weights and its application to fuzzy regression analysis Fuzzy Sets and Systems 57 1993 27 39
13. J. Jeng, C. Chuang, and S. Su Support vector interval regression networks for interval regression anlysis Fuzzy Sets and Systems 138 2003 283 300
14. G. Peters Fuzzy linear regression with fuzzy intervals Fuzzy Sets and Systems 63 1994 45 54
15. A.J. Smola, B. Schölkopf, A tutorial on support vector regression, Neuro-COLT2 Technical Report, Neuro-COLT, 1998.
16. H. Tanaka Fuzzy data analysis by possibilistic linear models Fuzzy Sets and Systems 24 1987 363 375
17. H. Tanaka, and H. Lee Interval regression analysis by quadratic programming approach IEEE Trans. Fuzzy Systems 6 1998 473 481
18. H. Tanaka, S. Uejima, and K. Asai Linear regression analysis with fuzzy model IEEE Trans. Systems Man, Cybern. 12 1982 903 907
19. H. Tanaka, and J. Watada Possibilistics linear systems and their applications to linear regression model Fuzzy Sets and Systems 27 1988 275 289
20. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer Berlin
21. V. Vapnik Statistical Learning Theory 1998 Wiley New York
22. G. Wahba Spline Models for Observational Data 1990 SIAM Philadelphia, PA