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@InProceedings{HlaCer2012b,
author = "Milan Hlad\'{\i}k and Michal {\v{C}}ern\'{y}",
editor = "Vo\v{r}echovsk\'{y} et al., M.",
feditor = "M. Vo\v{r}echovsk\'{y} and V. Sad\'{i}lek and S. Seitl and V. Vesel\'{y} and R. L. Muhanna and R. L. Mullen",
title = "On the tolerance approach to possibilistic nonlinear regression over interval data",
booktitle = "{REC 2012}, Proceedings of the 5th International Conference on Reliable Engineering Computing - Practical Applications and Practical Challenges, June 13-15, Brno",
publisher = "LITERA",
isbn = "978-80-214-4507-9",
pages = "183-195",
year = "2012",
bib2html_dl_pdf = "https://kam.mff.cuni.cz/~hladik/doc/2012-proc-REC-OnPossApprPossNonlinRegInt.pdf",
abstract = "We study the tolerance-based approach to possibilistic nonlinear regression models with interval data. We provide a method for determination of interval regression parameters of the model for the crisp input - interval output case and for the interval input - interval output case. We define two classes of nonlinear regression models for which efficient algorithms exist. We illustrate the theory by examples.",
keywords = "Interval regression, Nonlinear regression, Possibilistic regression, Tolerance quotient",
}