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Vol. 157, Issue 10, October 2013, pp. 374-381

 

Bullet

 

Study of Kernel Selection and Construction Kernel for SVM Classification
 
1,3 Lou XIONGWEI, 2 Huang Decai, 3 Fang LUMING, 3 Xu AIJUN

1 College of Information Engineering, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China

2 School of Computer Science & Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, 310032, China

3 College of Information Engineering, Zhejiang A & F University, Linan, Zhejiang, 311300, China
E-mail: lxwzjfc@163.com

 

Received: 3 July 2013   /Accepted: 25 September 2013   /Published: 31 October 2013

Digital Sensors and Sensor Sysstems

 

Abstract: : It is the most critical for finding the best kernel to apply the kernel-based algorithms in practice, such as support vector machines (SVMs) for classification. The selection is tightly connected to the encoding of our prior knowledge about the data and the pattern type of the task. In this paper, we discuss the kernel trick and its selection and construction methods for SVM classification. In addition we employed distance learning to construct RBF kernels. Experiments on several dataset proved the advantages of distance metric learning for kernel construction.

 

Keywords: Kernel, SVM, Distance learning.

 

 

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