Constructing multi-resolution support vector regression modelling

Authors:
Hong Peng;Zheng Pei;Jun Wang
Affiliations:
School of Mathematics & Computer Science, School of Electrical Information, Xihua University, Chengdu, Sichuan, China;School of Mathematics & Computer Science, School of Electrical Information, Xihua University, Chengdu, Sichuan, China;School of Mathematics & Computer Science, School of Electrical Information, Xihua University, Chengdu, Sichuan, China
Venue:
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
Year:
2005

Citing 5
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Advances in kernel methods
New Support Vector Algorithms

Neural Computation

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Abstract

Inspired by the theory of multi-resolution analysis of wavelet transform, combining advantages of multi-resolution theory and support vector machine, a new regression model that is called multi-resolution support vector regression (MR-SVR) for function regression is proposed in this paper. In order to construct MR-SVR, the scaling function at some scale and wavelets with different resolution is used as kernel of support vector machine, which is called multi-resolution kernel. The MR-SVR not only has the advantages of support vector machine, but also has the capability of multi-resolution which is useful to approximate nonlinear function. Simulation examples show the feasibility and effectiveness of the method.