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Interval Regression Analysis Using Support Vector Machine and Quantile Regression

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Fuzzy Systems and Knowledge Discovery (FSKD 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3613))

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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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© 2005 Springer-Verlag Berlin Heidelberg

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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

  • eBook Packages: Computer ScienceComputer Science (R0)

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