Abstract
SVMs tend to take a very long time to train with a large data set. If “redundant” patterns are identified and deleted in pre-processing, the training time could be reduced significantly. We propose a k-nearest neighbors(k-NN) based pattern selection method. The method tries to select the patterns that are near the decision boundary and that are correctly labeled. The simulations over synthetic data sets showed promising results: (1) By converting a non-separable problem to a separable one, the search for an optimal error tolerance parameter became unnecessary. (2) SVM training time decreased by two orders of magnitude without any loss of accuracy. (3) The redundant SVs were substantially reduced.
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
Almeida, M.B., Braga, A. and Braga J.P.(2000). SVM-KM: speeding SVMs learning with a priori cluster selection and k-means, Proc. Of the 6th Brazilian Symposium on Neural Networks, pp. 162–167
Boser, B.E., Guyon, I.M. and Vapnik, V.N. (1992). A training algorithm for optimal margin classifiers, In D. Haussler, Proc. Of the 5th Annual ACM workshop on Computaional Learning Theory, Pittsborgh, PA: ACM press
Burges, C.J.C., (1998). A Tutorial on Support Vector Machines for Pattern Recognition, Data Mining and Knowledge Discovery, vol. 2, pp. 121–167
Foody, G.M., (1999). The Significance of Border Training Patterns in Classification by a Feedforward Neural Network Using Back Propagation Learning, International Journal of Remote Sensing, vol. 20, no. 18, pp. 3549–3562
Gunn, S., (1998). Support Vector Machines for Classification and Regression, ISIS Technical Report
Lyhyaoui, A., Martinez, M., Mora, I., Vazquez, M., Sancho, J. and Figueiras-Vaidal, A.R., (1999). Sample Selection Via Clustering to Construct Support Vector-Like Classifiers, IEEE Transactions on Neural Networks, vol. 10, no. 6, pp. 1474–1481
Shin, H.J. and Cho, S.Z., (2002). Pattern Selection Using the Bias and Variance of Ensemble, Journal of the Korean Institute of Industrial Engineers, (to appear)
Vapnik, V., (1999). The Nature of Statistical Learning Theory, Springer. 2nd eds
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Shin, H., Cho, S. (2002). Pattern Selection for Support Vector Classifiers. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2002. IDEAL 2002. Lecture Notes in Computer Science, vol 2412. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45675-9_70
Download citation
DOI: https://doi.org/10.1007/3-540-45675-9_70
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44025-3
Online ISBN: 978-3-540-45675-9
eBook Packages: Springer Book Archive