Abstract
Support vector techniques were proposed by Vapnik as an alternative to neural networks for solving non-linear problems. The concepts of margins in support vector techniques provides a natural relationship with the rough set theory. This chapter describes rough set theoretic extensions of support vector technologies for classification, prediction, and clustering. The theoretical formulations of rough support vector machines, rough support vector regression, and rough support vector clustering are supported with a summary of experimental results.
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Lingras, P., Bhalchandra, P., Butz, C., Asharaf, S. (2013). Rough Support Vectors: Classification, Regression, Clustering. In: Skowron, A., Suraj, Z. (eds) Rough Sets and Intelligent Systems - Professor Zdzisław Pawlak in Memoriam. Intelligent Systems Reference Library, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30344-9_18
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DOI: https://doi.org/10.1007/978-3-642-30344-9_18
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