Abstract
In this paper, a simple method is proposed to reduce the number of support vectors (SVs) in the decision function. Because in practice the embedded data just lie into a subspace of the kernel-induced space, F, we can search a set of basis vectors (BVs) to express all the SVs according to the geometrical structure, the number of which is less than that of SVs. The experimental results show that our method can reduce the run-time complexity in SVM with the preservation of machine’s generalization, especially for the data of large correlation coefficients among input samples.
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
Vapnik, V.: The nature of statistical learning theory. Springer, Heidelberg (1995)
Burges, C.: Simplified support vector decision rules. In: Proc. 13th International Conference on Machine Learning, San Mateo, CA, pp. 71–77 (1996)
Schoelkopf, B., Mika, S., Burges, C., Knirsch, P., Muller, K., Ratsch, G., Smola, A.: Input space versus feature space in kernel-based methods. IEEE Trans. Neural Networks 10, 1000–1017 (1999)
Nguyen, D., Ho, T.: An efficient method for simplifying support machines. In: Proc. 22nd ICML, Bonn, Germany (2005)
Baudat, G., Anouar, F.: Feature vector selection and projection using kernels. Neurocomputing 5, 20–38 (2003)
Bengio, Y., Delalleau, O., Nicolas, L.: The curse of dimensionality for local kernel machines. Technical Report 1258, Dept. IRO, University of Montreal, Canada (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, B., Liu, H., Bao, Z. (2006). Speeding Up SVM in Test Phase: Application to Radar HRRP ATR. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893028_90
Download citation
DOI: https://doi.org/10.1007/11893028_90
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46479-2
Online ISBN: 978-3-540-46480-8
eBook Packages: Computer ScienceComputer Science (R0)