Rapid and brief communication: Learning the kernel parameters in kernel minimum distance classifier

Authors:
Daoqiang Zhang;Songcan Chen;Zhi-Hua Zhou
Affiliations:
National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China and Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, ...;Department of Computer Science and Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China;National Laboratory for Novel Software Technology, Nanjing University, Nanjing 210093, China
Venue:
Pattern Recognition
Year:
2006

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Abstract

Choosing appropriate values for kernel parameters is one of the key problems in many kernel-based methods because the values of these parameters have significant impact on the performances of these methods. In this paper, a novel approach is proposed to learn the kernel parameters in kernel minimum distance (KMD) classifier, where the values of the kernel parameters are computed through optimizing an objective function designed for measuring the classification reliability of KMD. Experiments on both artificial and real-world datasets show that the proposed approach works well on learning kernel parameters of KMD.