Abstract
This paper presents a new algorithm named Kernel Bisecting k-means and Sample Removal (KBK-SR) as a sampling preprocess for SVM training to improve the scalability. The novel top-down clustering approach Kernel Bisecting k-means in the KBK-SR tends to fast produce balanced clusters of similar sizes in the kernel feature space, which makes KBK-SR efficient and effective for reducing training samples for nonlinear SVMs. Theoretical analysis and experimental results on three UCI real data benchmarks both show that, with very short sampling time, our algorithm dramatically accelerates SVM training while maintaining high test accuracy.
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References
Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Fifth Annual Workshop on Computational Learning Theory. ACM Press, Pittsburgh, pp. 144–152 (1992)
Osuna, E., Freund, R., Girosi, F.: An improved training algorithm for support vector machines. In: ICNNSP 1997, New York, pp. 276–285 (1997)
Platt, J.: Fast training of support vector machines using sequential minimal optimization. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)
Joachims, T.: Making large-scale SVM learning practical. In: Schölkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, pp. 169–184. MIT Press, Cambridge (1999)
Schohn, G., Cohn, D.: Less is more: active learning with support vector machines. In: Proceedings of the 17th International Conference on Machine Learning (ICML 2000), pp. 839–846 (2000)
Lee, Y.J., Mangasarian, O.L.: RSVM: reduced support vector machines. In: Proceedings of the 1th SIAM International Conference on Data Mining, Chicago (2001)
Yu, H., Yang, J., Han, J.: Classifying large datasets using SVMs with hierarchical clusters. In: Proceedings of International Conference on Knowledge Discovery and Data Mining (KDD 2003), pp. 306–315 (2003)
Wang, D., Shi, L.: Selecting valuable training samples for SVMs via data structure analysis. Neurocomputing (2007). http://www.doi.org/10.1016/j.neucom.2007.09.008
Li, Y., Chung, S.M.: Parallel bisecting K-means with prediction clustering algorithm. J. Supercomput. 39(1), 19–37 (2007)
Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods. Cambridge University Press, Cambridge (2000)
Ruiz, A., López-de-Teruel, P.E.: Nonlinear kernel-based statistical pattern analysis. IEEE Trans. Neural Netw. 12, 16–32 (2001)
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Wang, TB. (2018). A Novel Kernel Clustering Method for SVM Training Sample Reduction. In: Hu, T., Wang, F., Li, H., Wang, Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11338. Springer, Cham. https://doi.org/10.1007/978-3-030-05234-8_7
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DOI: https://doi.org/10.1007/978-3-030-05234-8_7
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