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Online Support Vector Machine with a Single Pass for Streaming Data

Author

Listed:
  • Lisha Hu

    (Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China)

  • Chunyu Hu

    (School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan 250353, China)

  • Zheng Huo

    (Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China)

  • Xinlong Jiang

    (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China)

  • Suzhen Wang

    (Institute of Information Technology, Hebei University of Economics and Business, Shijiazhuang 050061, China)

Abstract

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.

Suggested Citation

  • Lisha Hu & Chunyu Hu & Zheng Huo & Xinlong Jiang & Suzhen Wang, 2022. "Online Support Vector Machine with a Single Pass for Streaming Data," Mathematics, MDPI, vol. 10(17), pages 1-24, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3113-:d:901832
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