Journal article

On optimal degree selection for polynomial kernel with support vector machines: Theoretical and empirical investigations

S Ali, K Smith-Miles

International Journal of Knowledge-Based and Intelligent Engineering Systems | Published : 2007

Abstract

The key challenge in kernel based learning algorithms is the choice of an appropriate kernel and its optimal parameters. Selecting the optimal degree of a polynomial kernel is critical to ensure good generalisation of the resulting support vector machine model. In this paper we propose Bayesian and Laplace approximation methods to estimate the polynomial degree. A rule based meta-learning approach is then proposed for automatic polynomial kernel and its optimal degree selection. The new approach is constructed and tested on different sizes of 112 datasets with binary class as well as multi class classification problems. An extensive computational evaluation of these methods is conducted, and..

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University of Melbourne Researchers

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