Abstract
In this paper a new family of kernel functions for SVM classifiers, based on a statistically–induced measure of distance between observations in the pattern space, is proposed and experimentally evaluated in the context of binary classification problems. The application of the proposed approach improves the accuracy of results compared to the case of training without postulated enhancements.
Numerical results outperform those of the SVM with Gaussian and Laplace kernels.
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Dendek, C., Mańdziuk, J. (2012). Statistically–Induced Kernel Function for Support Vector Machine Classifier. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7267. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29347-4_43
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DOI: https://doi.org/10.1007/978-3-642-29347-4_43
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