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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Yang, Z., Guo, J., Xu, W., Nie, X., Wang, J., Lei, J. (2006). Multi-scale Support Vector Machine for Regression Estimation. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_151
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DOI: https://doi.org/10.1007/11759966_151
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
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