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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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
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