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A novel online LS-SVM approach for regression and classification
Authors
Selami Beyhan
Erdem Dilmen
Publication date
1 January 2017
Publisher
'Elsevier BV'
Doi
Cite
Abstract
In this paper, a novel online least squares support vector machine approach is proposed for classification and regression problems. Gaussian kernel function is used due to its strong generalization capability. The contribution of the paper is twofold. As the first novelty, all parameters of the SVM including the kernel width parameter σ are trained simultaneously when a new sample arrives. Unscented Kalman filter is adopted to train the parameters since it avoids the sub-optimal solutions caused by linearization in contrast to extended Kalman filter. The second novelty is the variable size moving window by an intelligent update strategy for the support vector set. This provides that SVM model captures the dynamics of data quickly while not letting it become clumsy due to the big amount of useless or out-of-date support vector data. Simultaneous training of the kernel parameter by unscented Kalman filter and intelligent update of support vector set provide significant performance using small amount of support vector data for both classification and system identification application results. © 201
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Pamukkale Üniversitesi Açık Erişim Arşivi
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oai:acikerisim.pau.edu.tr:1149...
Last time updated on 05/09/2019
Pamukkale Üniversitesi Açık Erişim Arşivi
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:acikerisim.pau.edu.tr:1149...
Last time updated on 05/09/2019
Pamukkale Üniversitesi Açık Erişim Arşivi
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:acikerisim.pau.edu.tr:1149...
Last time updated on 05/09/2019