Automatic parameter selection for polynomial kernel

被引：27

作者：

Ali, S ^{[1
]}

Smith, KA ^{[1
]}

机构：

[1] Monash Univ, Sch Business Syst, Clayton, Vic 3800, Australia

来源：

PROCEEDINGS OF THE 2003 IEEE INTERNATIONAL CONFERENCE ON INFORMATION REUSE AND INTEGRATION | 2003年

关键词：

SVM; polynomial kernel; data distribution; Bayesian inference;

D O I：

10.1109/IRI.2003.1251420

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：243 / 249

页数：7