Abstract
Conventional Support Vector Regression (SVR) is not capable of online setting and its training algorithm is inefficient in real-time applications. Through analyzing the possible variation of support vector sets after new samples are added to the training set, and extending the incremental support vector machine for classification, an online learning algorithm for SVR is proposed. To illustrate the favorable performance of the online learning algorithm, a nonlinear system identification experiment is considered. The simulation results indicate that the learning efficiency and prediction accuracy of the online learning algorithm are higher than that of the existing algorithms, and it is more suitable for system identification.
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References
Wang, L.P.: Support Vector Machines: Theory and Application. Springer, Heidelberg (2005)
Smola, A.J., Schökopf, B.: A tutorial on support vector regression. NeuralCOLT2 Technical Report Series, No. NC2-TR-98-030. London, Royal Holloway Colledge, University of London (1998)
Cauwenberghs, G., Poggio, T.: Incremental and decremental support vector machine learning. In: Dietterich, T.G., Leen, T.K., Tresp, V. (eds.) Advances in Neural Information Processing Sytems, pp. 409–415. MIT Press, Cambridge (2001)
Gentile, C.: A new approximate maximal margin classification algorithm. Journal of Machine Learning Research 2, 213–242 (2001)
Ralaivola, L., d’Alche-Buc, F.: Incremental support vector machine learning: A local approach. In: Dorffner, G., Bischof, H., Hornik, K. (eds.) ICANN 2001. LNCS, vol. 2130, pp. 322–330. Springer, Heidelberg (2001)
Syed, N., Liu, H., Sung, K.K.: Incremental Learning with Support Vector Machines. In: Proceedings of the Workshop on Support Vector Machines at the International Joint Conference on Artificial Intelligence, Stockholm, Sweden (1999)
Martin, M.: On-line support vector machine regression. In: Elomaa, T., Mannila, H., Toivonen, H. (eds.) ECML 2002. LNCS (LNAI), vol. 2430, pp. 282–294. Springer, Heidelberg (2002)
Vapnik, V., Golowich, S., Smola, A.: Support vector method for function approximation, regression estimation, and signal processing. In: Mozer, M.C., Jordan, M.I., Petsche, T. (eds.) Advances in Neural Information Processing Systems, pp. 281–287. MIT Press, Cambridge (1997)
Narendra, K., Parthasarathy, K.: Identification and Control of Dynamical Systems Using Neural Networks. IEEE Trans. on Neural Networks 1, 4–27 (1990)
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Yu, Z., Fu, X., Li, Y. (2005). Online Support Vector Regression for System Identification. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_89
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DOI: https://doi.org/10.1007/11539117_89
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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