On optimal degree selection for polynomial kernel with support vector machines : Theoretical and empirical investigations
journal contribution
posted on 2017-12-06, 00:00 authored by A B M Shawkat Ali, K Smith-MilesThe 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 rules are generated to determine when these approximation methods are appropriate.
Funding
Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)
History
Volume
11Issue
1Start Page
1End Page
18Number of Pages
18eISSN
1875-8827ISSN
1327-2314Location
NetherlandsPublisher
IOS PressPublisher DOI
Language
en-ausPeer Reviewed
- Yes
Open Access
- No
Era Eligible
- Yes