A Novel Multi-class Support Vector Machine Based on Fuzzy Theories
Support vector machine (SVM), proposed by Vapnik based on statistical learning theory, is a novel machine learning method. However, there are two problems to be solved in this field: one is the multi-class classification problem, and the other is the sensitivity to the noisy data. In order to overcome these difficulties, a novel method of fuzzy compensation multi-class support vector machine, named as FC-SVM, is proposed in this paper. This method imports a fuzzy compensation function to the penalty in the straightly construction multi-class SVM classification problem proposed by Weston and Watkins. Aim at the dual affects to classification results by each input data, this method has punish item be fuzzy, compensates weight to classification, reconstructs the optimization problem and its restrictions, reconstructs Langrage formula, and presents the theories deduction. This method is applied to the benchmark data sets. The experiment presents our method is feasible.
KeywordsSupport Vector Machine Support Vector Machine Algorithm Support Vector Machine Training Fuzzy Support Vector Machine Theory Deduction
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- 2.Hsu, C.W., Lin, C.J.: A Comparison of Methods for Multi-class Support Vector Machines. IEEE Transactions on Neural Networks 2, 415–425 (2002)Google Scholar
- 3.Xu, J.H., Zhang, X.G., Li, Y.D.: Advances in Support Vector Machines. Control and Decision 5, 481–484 (2004)Google Scholar
- 4.Platt, J.C., Cristianini, N., Shawe-Taylor, J.: Large Margin DAG’s for Multiclass Classification. In: Advances in Neural Information Processing Systems, vol. 12, pp. 547–553. MIT Press, Cambridge (2000)Google Scholar
- 5.Weston, J., Watkins, C.: Multi-class Support Vector Machines. Department of Computer Science, Royal Holloway University of London Technical Report, SD2TR298204 (1998)Google Scholar
- 6.Platt, J.C.: Fast Training of Support Vector Machines Using Sequential Minimal Optimization. In: Advances in Kernel Methods –Support Vector Learning, pp. 185–208. MIT Press, Cambridge (1999)Google Scholar
- 7.Vapnik, V.N.: The Nature of Statistical Learning Theory, 2nd edn. Springer, Heidelberg (1999)Google Scholar
- 11.Inoue, T., Abe, S.: Fuzzy Support Vector Machines for Pattern Classification. In: Proceedings of International Joint Conference on Neural Networks, pp. 1449–1454 (2001)Google Scholar
- 12.Lin, C.F., Wang, S.D.: Fuzzy Support Vector Machines. IEEE Transactions on Neural Networks 2, 464–471 (2002)Google Scholar
- 13.Sun, Z.H., Sun, Y.X.: Fuzzy Support Vector Machine for Regression Estimation. In: IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 3336–3341 (2003)Google Scholar
- 16.Kecman, V.: Learning and Soft Computing. In: Support Vector Machines, Neural Networks and Fuzzy Logic Models, The MIT Press, Cambridge (2001)Google Scholar
- 18.Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Heidelberg (2005)Google Scholar