Abstract
This paper presents a data based kernel selection approach, which utilizes the geometry distribution of data. Once the approximate distribution can be confirmed as a special one like circle, cirque, sphere cylinder, et al, some known kernel functions corresponding to the special distribution can then be used. Four datasets are used to verify the presented approach, and simulation results demonstrate the rationality and effectiveness of the presented approach.
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.
Similar content being viewed by others
References
Anguita, D., Boni, A., Ridella, S.: Evaluating the generalization ability of support vector machines through the bootstrap. Neural Processing Letters 11, 51–58 (2000)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Burges, C.J.C.: Geometry and invariance in kernel based method. In: Scholkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods, pp. 89–116. MIT Press, Cambridge (1999)
Chapelle, O., Vapnik, V.: Model selection for support vector machines. In: Smola, A., Leen, T., Muller, K. (eds.) Advances in Neural Information Processing Systems 12, MIT Press, Cambridge (2000)
Cox, T., et al.: Multidimensional Scaling. Chapman and Hall, London (1994)
Drucker, H., Burges, C.J.C., Kaufman, L., Smola, A., Vapnik, V.: Support vector regression machines. In: Mozer, M., Jordan, M., Petsche, J. (eds.) Advances in Neural Information Processing Systems 9, pp. 155–161. MIT Press, Cambridge (1997)
Gao, J.B., Gunn, S.R., Harris, C.J.: A probabilitic framework for SVM regression and error bar estimation. Machine Learning 46, 71–89 (2002)
Gunn, S.: Support vector machines classification and regression. ISIS Technical Report, Image Speech & Intelligent Systems Group, University of Southampton (1998)
Jabri, M.: Robust Principle Component Analysis. IEEE Transaction on Neural Network 1, 289–298 (2000)
Mattera, D., Haykin, S.: Support vector machines for dynamic reconstruction of a chaotic system. In: Scholkopf, B., Burges, C.J.C., Smola, A. (eds.) Advances in Kernel Methods-Support Vector Learning, pp. 211–242. MIT Press, Cambridge (1999)
Müller, K.R., Smola, A., Ratsch, G., Scholkopf, B., Kohlmorgen, J., Vapnik, V.: Predicting time series with support vector machines. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 999–1004. Springer, Heidelberg (1997)
Vapnik, V.: The Nature of Statistical Learning Theory. Springer, New York (1995)
Vapnik, V.: Statistical Learning Theory. Wiley, New York (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, W., Guo, J., Men, C. (2008). An Approach for Kernel Selection Based on Data Distribution. In: Wang, G., Li, T., Grzymala-Busse, J.W., Miao, D., Skowron, A., Yao, Y. (eds) Rough Sets and Knowledge Technology. RSKT 2008. Lecture Notes in Computer Science(), vol 5009. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79721-0_80
Download citation
DOI: https://doi.org/10.1007/978-3-540-79721-0_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-79720-3
Online ISBN: 978-3-540-79721-0
eBook Packages: Computer ScienceComputer Science (R0)