Authors: Naima Djelloul; Abdessamad Amir
Addresses: Department of Mathematics and Informatics, Mostaganem University, Mostaganem, Algeria ' Department of Mathematics and Informatics, Mostaganem University, Mostaganem, Algeria
Abstract: For several types of machines learning problems, the support vector machine is a method of choice. The kernel functions are a basic ingredient in support vector machine theory. Kernels based on the concepts of orthogonal polynomials gave the great satisfaction in practice. In this paper we identify the reproducing kernel Hilbert space of legendre polynomial kernel which allows us to understand its ability to extract more discriminative features. We also show that without being a universal kernel, legendre kernel possesses the same separation properties. The legendre, Gaussian and polynomial kernel performance has been first evaluated on two dimensional illustrative examples in order to give a graphical comparison, then on real world data sets from UCI repository. For nonlinearly separable data, legendre kernel always gives satisfaction regarding classification accuracy and reduction in the number of support vectors.
Keywords: support vector machine; SVM; kernel trick; reproducing kernel Hilbert space; orthogonal polynomials; tensor product.
International Journal of Computing Science and Mathematics, 2019 Vol.10 No.6, pp.580 - 595
Available online: 05 Dec 2019 *Full-text access for editors Access for subscribers Purchase this article Comment on this article