Abstract
This paper deals with interval regression analysis using support vector machine and quantile regression method. The algorithm consists of two phases – the identification of the main trend of the data and the interval regression based on acquired main trend. Using the principle of support vector machine the linear interval regression can be extended to the nonlinear interval regression. Numerical studies are then presented which indicate the performance of this algorithm.
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Hwang, C., Hong, D.H., Na, E., Park, H., Shim, J. (2005). Interval Regression Analysis Using Support Vector Machine and Quantile Regression. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_12
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DOI: https://doi.org/10.1007/11539506_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28312-6
Online ISBN: 978-3-540-31830-9
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