Abstract
Support vector regression (SVR) is an important tool for data mining. In this paper, we first introduce a new way to make SVR have the similar mathematic form as that of support vector classification. Then we propose a versatile iterative method, successive overrelaxation, for the solution of extremely large regression problems using support vector machines. Experiments prove that this new method converges considerably faster than other methods that require the presence of a substantial amount of the data in memory.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
J. Platt. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. Burges, and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning, MIT Press, 1998
E. Osuna, R. Freund, and F. Girosi. An improved training algorithm for support vector machines. In Proc. of IEEE NNSP’s97, 1997
Olvi L. Mangasarian and David R. Musicant. Successive overrelaxation for support vector machines. IEEE Trans on Neural Networks, 1999, 10(5): 1032–1037
C.J.C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 1998, 2(2)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Quan, Y., Yang, J., Ye, C. (2003). Successive Overrelaxation for Support Vector Regression. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_109
Download citation
DOI: https://doi.org/10.1007/3-540-39205-X_109
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-14040-5
Online ISBN: 978-3-540-39205-7
eBook Packages: Springer Book Archive