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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (2001)
Cortes, C., Vapnik, V.: Support Vector Networks. Machine Learning 20, 273–297 (1995)
Smola, A., Schölkopf, B.: A Tutorial on Support Vector Regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK (1998)
Schölkopf, B., Smola, A., Williamson, R.C., Bartlett, P.L.: New Support Vector Algorithms. Neural Computation 20, 1207–1245 (2000)
Bennett, K.: Combining Support Vector and Mathematical Programming Methods for Induction. In: Schölkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods - SV Learning, pp. 307–326. MIT Press, Cambridge (1999)
Smola, A.: Learning with Kernels [PhD thesis]. Technische Universitat Berlin (1998)
Weston, J., Gammerman, A., Stitson, M., Vapnik, V., Vork, V., Watkins, C.: Support Vector Density Estimation. In: Schölkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods - SV Learning, pp. 293–306. MIT Press, Cambridge (1999)
Zhang, Q., Benveniste, A.: Wavelet Networks. IEEE Trans. Neural Networks 3, 889–898 (1992)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Peng, H., Pei, Z., Wang, J. (2005). Constructing Multi-resolution Support Vector Regression Modelling. In: Zhang, S., Jarvis, R. (eds) AI 2005: Advances in Artificial Intelligence. AI 2005. Lecture Notes in Computer Science(), vol 3809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11589990_116
Download citation
DOI: https://doi.org/10.1007/11589990_116
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30462-3
Online ISBN: 978-3-540-31652-7
eBook Packages: Computer ScienceComputer Science (R0)