Abstract
In this article, we propose a novel regression method which is based solely on Support Vector Classification. Experiments show that the new method has comparable or better generalization performance than ε-insensitive Support Vector Regression. The tests were performed on synthetic data, on various publicly available regression data sets, and on stock price data. Furthermore, we demonstrate how a priori knowledge which has been already incorporated to Support Vector Classification for predicting indicator functions, could be directly used for a regression problem.
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Orchel, M. (2011). Regression Based on Support Vector Classification. In: Dobnikar, A., Lotrič, U., Šter, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2011. Lecture Notes in Computer Science, vol 6594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20267-4_37
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DOI: https://doi.org/10.1007/978-3-642-20267-4_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-20266-7
Online ISBN: 978-3-642-20267-4
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