Jianehua Liu, Jia-nin Chen, Shan Jiane, and Junshi Cheng, Online LS-SVM for function estimation and classification, J. Univ. Sci. Technol. Beijing, 10(2003), No. 5, pp. 73-77.
Cite this article as:
Jianehua Liu, Jia-nin Chen, Shan Jiane, and Junshi Cheng, Online LS-SVM for function estimation and classification, J. Univ. Sci. Technol. Beijing, 10(2003), No. 5, pp. 73-77.
Jianehua Liu, Jia-nin Chen, Shan Jiane, and Junshi Cheng, Online LS-SVM for function estimation and classification, J. Univ. Sci. Technol. Beijing, 10(2003), No. 5, pp. 73-77.
Citation:
Jianehua Liu, Jia-nin Chen, Shan Jiane, and Junshi Cheng, Online LS-SVM for function estimation and classification, J. Univ. Sci. Technol. Beijing, 10(2003), No. 5, pp. 73-77.
An online algorithm for training LS-SVM (Least Square Support Vector Machines) was proposed for the application of function estimation and classification. Online LS-SVM means that LS-SVM can be trained in an incremental way, and can be pruned to get sparse approximation in a decremental way. When a SV (Support Vector) is added or removed, the online algorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Online algorithm is especially useful to realistic function estimation problem such as system identification. The experiments with benchmark function estimation problem and classification problem show the validity of this online algorithm.
An online algorithm for training LS-SVM (Least Square Support Vector Machines) was proposed for the application of function estimation and classification. Online LS-SVM means that LS-SVM can be trained in an incremental way, and can be pruned to get sparse approximation in a decremental way. When a SV (Support Vector) is added or removed, the online algorithm avoids computing large-scale matrix inverse. Thus the computation cost is reduced. Online algorithm is especially useful to realistic function estimation problem such as system identification. The experiments with benchmark function estimation problem and classification problem show the validity of this online algorithm.
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