New approach to interval linear regression

24th Mini EURO Conference on Continuous Optimization and Information-Based Technologies in the Financial Sector, MEC EurOPT 2010

Volumn , Issue , 2010, Pages 167-171

  Hladík, Milan ^a   Černý, Michal ^b  

^a CHARLES UNIVERSITY   (Czech Republic)

^b UNIVERSITY OF ECONOMICS   (Czech Republic)

Author keywords

Fuzzy regression; Interval regression; Possibilistic regression

Indexed keywords

FITS AND TOLERANCES; LINEAR SYSTEMS; OPTIMIZATION;

DIVERSE METHODS; FITNESS MEASURES; FUZZY REGRESSIONS; INTERVAL REGRESSION; INTERVAL REGRESSION ANALYSIS; POSSIBILISTIC REGRESSIONS; REGRESSION PARAMETERS; TOLERANCE ANALYSIS;

REGRESSION ANALYSIS;

EID: 79952015525     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Alefeld, G.; Herzberger, J. 1983. Introduction to interval computations. Computer Science and Applied Mathematics. New York: Academic Press.
2. Chang, P.-T.; Lee, E. S.; Konz, S. A. 1996. Applying fuzzy linear regression to VDT legibility, Fuzzy Sets and Systems 80(2): 197-204. doi:10.1016/0165-0114(95)00153-0.
3. Hao, P.-Y. 2009. Interval regression analysis using support vector networks, Fuzzy Sets and Systems 160(17): 2466- 2485. doi:10.1016/j.fss.2008.10. 012.
4. Heshmaty, B.; Kandel, A. 1985. Fuzzy linear regression and its applications to forecasting in uncertain environment. Fuzzy Sets and Systems 15(2): 159-191. doi:10.1016/0165-0114(85)90044-2.
5. Hladík, M. 2008. Tolerance analysis in linear programming. Technical report KAM-DIMATIA Series, 2008-901, Prague: Department of Applied Mathematics at Charles University.
6. Huang, C.-H.; Kao, H.-Y. 2009. Interval regression analysis with soft-margin reduced support vector machine. Lecture Notes in Computer Science, 5579, LNAI, 826-835.
7. Kaneyoshi, M.; Tanaka, H.; Kamei, M.; Furuta, H. 1990. New system identification technique using fuzzy regression analysis, in Proceedings of the First International Symposium on Uncertainty Modeling and Analysis. Baltimore, pp. 528-533.
8. Lee, H.; Tanaka, H. 1999. Upper and lower approximation models in interval regression using regression quantile techniques, European Journal of Operational Research 116(3): 653-666. doi:10.1016/S0377-2217(98)00191-X.
9. Liu, P. 2009. Study on a speech learning approach based on interval support vector regression, in Proceedings of 4th International Conference on Computer Science & Education. Nanning, pp. 1009-1012.
10. Tanaka, H. 1987. Fuzzy data analysis by possibilistic linear models, Fuzzy Sets and Systems 24(3): 363-375. doi:10.1016/0165-0114(87)90033-9.
11. Tanaka, H.; Lee, H. 1998. Interval regression analysis by quadratic programming approach, IEEE Transactions on Fuzzy Systems 6(4): 473-481. doi:10.1109/91.728436.
12. Tanaka, H.; Uejima, S.; Asai, K. 1982. Linear regression analysis with fuzzy model, IEEE Transactions on Systems, Man, and Cybernetics, SMC 12(6): 903-907.
13. Tanaka, H.; Watada, J. 1988. Possibilistic linear systems and their application to the linear regression model. Fuzzy Sets and Systems 27(3): 275-289. doi:10.1016/0165-0114(88)90054-1.
14. Ward, J.E.; Wendell, R.E. 1990. Approaches to sensitivity analysis in linear programming, Annals of Operations Research 27: 3-38. doi:10.1007/ BF02055188.