Automatic parameter selection for polynomial kernel
Ali, S.
Smith, K.A.
Sch. of Bus. Syst., Monash Univ., Vic., Australia;
This paper appears in: Information Reuse and Integration, 2003. IRI 2003. IEEE International Conference on
Publication Date: 27-29 Oct. 2003
On page(s): 243- 249
ISSN:
ISBN: 0-7803-8242-0
INSPEC Accession Number: 7862041
Posted online: 2004-01-08 13:25:19.0
Abstract
Kernel is the heart of kernel based learning. To choose an appropriate parameter for a specific kernel is an important research issue in the data mining area. In this paper, we propose an automatic parameter selection approach for polynomial kernel. The algorithm is tested on support vector machines (SVM). The parameter selection is considered on the basis of prior information of the data distribution and Bayesian inference. The new approach is tested on different sizes of benchmark datasets with binary class problems as well as multi class classification problems.
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