Abstract
Support vector machines are currently very popular approaches to supervised learning. Unfortunately, the computational load for training and classification procedures increases drastically with size of the training data set. In this paper, a method called cooperative clustering is proposed. With this procedure, the set of data points with pre-determined size near the border of two classes is determined. This small set of data points is taken as the set of support vectors. The training of support vector machine is performed on this set of data points. With this approach, training efficiency and classification efficiency are achieved with small effects on generalization performance. This approach can also be used to reduce the number of support vectors in regression problems.
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References
Vapnik, V.N.: Statistical Learning Theory. Join Wiley and Sons, New York (1998)
Boser, B., Guyon, I., Vapnik, V.N.: A Training Algorithm for Optimal Margin Classifiers. In: Haussler, D. (ed.) Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pp. 144–152. ACM Press, New York (1992)
Osuna, E., Freund, R., Girosi, F.: An Improved Training Algorithm for Support Vector Machines. In: Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 276–285 (1997)
Platt, J.C.: Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines. Technical Report MSR-TR-98-14, Microsoft Research (1998)
Suykens, J.A.K., Vandewalle, J.: Least Square Support Vector Machine Classifiers. Neural Processing Letters 9(3), 293–300 (1999)
Burges, C.J.C.: Simplified Support Vector Decision Rules. In: Saitta, L. (ed.) Proceedings of 13th International Conference on Machine Learning, San Mateo, CA, pp. 71–77. Morgan Kaufmann Publishers, Inc., San Francisco (1996)
Downs, T., Gates, K.E., Masters, A.: Exact Simplification of Support Vector Solutions. Journal of Machine Learning Research 2, 293–297 (2001)
Lin, K., Lin, C.: A Study on Reduced Support Vector Machines. IEEE Transactions on Neural Networks 14(6), 1449–1459 (2003)
Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
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© 2006 Springer-Verlag Berlin Heidelberg
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Tian, S., Mu, S., Yin, C. (2006). Cooperative Clustering for Training SVMs. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3971. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11759966_141
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DOI: https://doi.org/10.1007/11759966_141
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34439-1
Online ISBN: 978-3-540-34440-7
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