Abstract
In this article we present a new polynomial function that can be used as a kernel for Support Vector Machines (SVMs) in binary classification and regression problems. We prove that this function fulfills the mathematical properties of a kernel. We consider here a set of SVMs based on this kernel with which we perform a set of experiments. Their efficiency is measured against some of the most popular kernel functions reported in the past.
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Mejía-Guevara, I., Kuri-Morales, Á. (2007). MP-Polynomial Kernel for Training Support Vector Machines. In: Rueda, L., Mery, D., Kittler, J. (eds) Progress in Pattern Recognition, Image Analysis and Applications. CIARP 2007. Lecture Notes in Computer Science, vol 4756. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76725-1_61
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DOI: https://doi.org/10.1007/978-3-540-76725-1_61
Publisher Name: Springer, Berlin, Heidelberg
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