General kernel optimization model based on kernel fisher criterion

Authors:
Bo Chen;Hongwei Liu;Zheng Bao
Affiliations:
National Lab of Radar Signal Processing, Xidian University, Shaanxi, P.R. China;National Lab of Radar Signal Processing, Xidian University, Shaanxi, P.R. China;National Lab of Radar Signal Processing, Xidian University, Shaanxi, P.R. China
Venue:
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Year:
2006

Citing 2
Cited 0

Improving support vector machine classifiers by modifying kernal functions

Neural Networks
Optimizing the kernel in the empirical feature space

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper a general kernel optimization model based on kernel Fisher criterion (GKOM) is presented. Via a data-dependent kernel function and maximizing the kernel Fisher criterion, the combination coefficients of different kernels can be learned adaptive to the input data. Finally positive empirical results on benchmark datasets are reported.