Multi-scale support vector machine for regression estimation

Authors:
Zhen Yang;Jun Guo;Weiran Xu;Xiangfei Nie;Jian Wang;Jianjun Lei
Affiliations:
PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China;PRIS Lab, School of Information Engineering, Beijing University of Posts and Telecommunications, Beijing, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part I
Year:
2006

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Abstract

Recently, SVMs are wildly applied to regression estimation, but the existing algorithms leave the choice of the kernel type and kernel parameters to the user. This is the main reason for regression performance degradation, especially for the complicated data even the nonlinear and non-stationary data. By introducing the ‘empirical mode decomposition (EMD)’ method, with which any complicated data set can be decomposed into a finite and often small number of ‘intrinsic mode functions’ (IMFs) based on the local characteristic time scale of the data, this paper propose an important extension to the SVM method: multi-scale support vector machine based on EMD, in which several kernels of different scales can be used simultaneously to approximate the target function in different scales. Experiment results demonstrate the effectiveness of the proposed method.