Online LS-SVM for function estimation and classification

Jianehua Liu, Jia-nin Chen, Shan Jiane, Junshi Cheng

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    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.
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    Automation

    Online LS-SVM for function estimation and classification

    • Information Storage Research Center, Shanghai Jiaotong University, Shanghai 200030, China

    • Makuhari Systems Laboratory 1-9-3 Nakase, Mihama-ku, Chiba, Chiba 261-8588, Japan

    基金项目: 

    This project was financially supported by the National Natural Science Foundation of China (No. 69889050).

      通信作者:

      Jianehua Liu E-mail: jhliu99@163.com

    中文摘要

    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.

     

    Automation

    Online LS-SVM for function estimation and classification

    Author Affilications

      • Information Storage Research Center, Shanghai Jiaotong University, Shanghai 200030, China

      • Makuhari Systems Laboratory 1-9-3 Nakase, Mihama-ku, Chiba, Chiba 261-8588, Japan

    • Funds: 

      This project was financially supported by the National Natural Science Foundation of China (No. 69889050).

    • Received: 27 November 2002;
    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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