Abstract
Proximal SVM (PSVM), which is a variation of standard SVM, leads to an extremely faster and simpler algorithm for generating a linear or nonlinear classifier than classical SVM. An efficient incremental method for linear PSVM classifier has been introduced, but it can’t apply to nonlinear PSVM and incremental technique is the base of online learning and large data set training. In this paper we focus on the online learning problem. We develop an incremental learning method for a new nonlinear PSVM classifier, utilizing which we can realize online learning of nonlinear PSVM classifier efficiently. Mathematical analysis and experimental results indicate that these methods can reduce computation time greatly while still hold similar accuracy.
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© 2007 Springer Berlin Heidelberg
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Liu, Q., He, Q., Shi, Z. (2007). Incremental Nonlinear Proximal Support Vector Machine. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72395-0_43
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DOI: https://doi.org/10.1007/978-3-540-72395-0_43
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72394-3
Online ISBN: 978-3-540-72395-0
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