An efficient method for tuning kernel parameter of the support vector machine

Author

Debnath, Rameswar ; Takahashi, Haruhisa

Author_Institution

Dept. of Inf. & Commun. Eng., Univ. of Electro-Commun., Tokyo, Japan

Volume

2

fYear

2004

fDate

26-29 Oct. 2004

Firstpage

1023

Abstract

We propose a new method for searching the kernel parameter of the support vector machine on the basis of the distribution of data in the feature space. Although the distribution (structure) of data is unknown in the feature space, it depends on the kernel parameter. The distribution of data is characterized by the principal component analysis method. Thus, simple eigenanalysis method is applied to the matrix of the same dimension as the kernel matrix to find the kernel parameter. Therefore, this method is very fast. The proposed method can obtain the kernel parameter graphically.

Keywords

eigenvalues and eigenfunctions; matrix algebra; principal component analysis; search problems; support vector machines; tuning; data distribution; eigenanalysis method; feature space; graphical method; kernel matrix; kernel parameter; principal component analysis; searching; support vector machine; tuning; Iterative methods; Kernel; Minimization methods; Neural networks; Newton method; Programming; Search methods; Support vector machines; Training data;

fLanguage

English

Publisher

ieee

Conference_Titel

Communications and Information Technology, 2004. ISCIT 2004. IEEE International Symposium on

Print_ISBN

0-7803-8593-4

Type

conf

DOI

10.1109/ISCIT.2004.1413874

Filename

1413874