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. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Mejía-Guevara, I., & Kuri-Morales, Á. (2007). MP-polynomial kernel for training support vector machines. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4756 LNCS, pp. 584–593). https://doi.org/10.1007/978-3-540-76725-1_61
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