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An Improved Method for Kernel Function withData-Dependent Type of Support Vector Machine

SUN Yan-feng^1,2, LIANG Yan-chun²

1. College of Mathematics Science, Jilin University, Changchun 130012, China； 2. College of Computer Science and Technology, Jilin University, Changchun 130012, China

Received:2003-01-04 Revised:1900-01-01 Online:2003-07-26 Published:2003-07-26
Contact: LIANG Yan-chun

Abstract

Abstract: A novel improved method for kernel function of support vector machine(SVM) is proposed based on the information geometry theory. The kernel function is modified by means of a conformal mapping, which makes the kernel function data-dependent. The simulated results on the prediction of stock price show that the improved approach possesses better forecasting precision than the conventional models.

Key words: support vector machine, data dependent, information geometry, conformal mapping

CLC Number:

TP183

SUN Yan-feng, LIANG Yan-chun. An Improved Method for Kernel Function withData-Dependent Type of Support Vector Machine[J].J4, 2003, 41(03): 329-333.

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An Improved Method for Kernel Function withData-Dependent Type of Support Vector Machine

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