Abstract
Reduced set method is an important approach to speed up classification process of support vector machine (SVM) by compressing the number of support vectors included in the machine’s solution. Existing works find the reduced set vectors based on solving an unconstrained optimization problem with multivariables, which may suffer from numerical instability or get trapped in a local minimum. In this paper, a novel reduced set method relying on kernel-based clustering is presented to simplify SVM solution. This approach is conceptually simpler, involves only linear algebra and overcomes the difficulties existing in former reduced set methods. Experiments on real data sets indicate that the proposed method is effective in simplifying SVM solution while preserving machine’s generalization performance.
Keywords
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.: Statistical Learning Theory. Wiley, New York (1998)
Burges, C.J.C.: Simplified Support Vector Decision Rules. In: 13th International Conference on Machine Learning, pp. 71–77 (1996)
Schoelkopf, B., Mika, S., Burges, C.J.C., Knirsch, P., Muller, K., Ratsch, G., Smola, A.J.: Input space versus feature space in kernel-based methods. IEEE Trans. Neural Netw. 10(5), 1000–1017 (1999)
Schoelkopf, B., Smola, A.: Learning with kernels. MIT Press, Cambridge (2002)
Mika, S., Scholkopf, B., Smola, A., Muller, K., Scholz, M., Ratsch, G.: Kernel PCA and denoising in feature spaces. In: Advances in Neural Information Processing Systems, vol. 11, Morgan Kaufmann, San Mateo (1998)
Xiao-Li, L., Ji-Min, L., Zhong-Zhi, S.: A Chinese Web Page Classifier Based on Support Vector Machine and Unsupervised Clustering. Chinese J. Computers, 62–68 (January 2001)
Kwok, J.T., Tsang, I.W.: The pre-image problem in kernel methods. IEEE Transactions on Neural Networks 15, 1517–1525 (2004)
Williams, C.K.: On a connection between kernel PCA and metric multidimensional scaling. Machine Learning 46, 11–19 (2002)
Gower, J.C.: Adding a point to vector diagrams in multivariate analysis. Biometrika 55, 582–585 (1968)
Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/cjlin/libsvm
Murphy, P.M., Aha, D.W.: UCI repository of machine learning databases. Irvine, CA (1994), available at http://www.ics.uci.edu/~mlearn/MLRepository.html
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
Zeng, ZQ., Gao, J., Guo, H. (2006). Simplified Support Vector Machines Via Kernel-Based Clustering. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_146
Download citation
DOI: https://doi.org/10.1007/11941439_146
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
eBook Packages: Computer ScienceComputer Science (R0)