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A Class of Convergent Parallel Algorithms for SVMs Training

Author

Listed:
  • Andrea Manno

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Laura Palagi

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

  • Simone Sagratella

    (Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza")

Abstract

The training of Support Vector Machines may be a very difficult task when dealing with very large datasets. The memory requirement and the time consumption of the SVMs algorithms grow rapidly with the increase of the data. To overcome these drawbacks a lot of parallel algorithms have been implemented, but they lack of convergence properties. In this work we propose a generic parallel algorithmic scheme for SVMs and we state its asymptotical global convergence under suitable conditions. We outline how these assumptions can be satisfied in practice and we suggest various specific implementations exploiting the adaptable structure of the algorithmic model.

Suggested Citation

  • Andrea Manno & Laura Palagi & Simone Sagratella, 2014. "A Class of Convergent Parallel Algorithms for SVMs Training," DIAG Technical Reports 2014-17, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    • Handle: RePEc:aeg:report:2014-17
    as

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    File URL: http://www.dis.uniroma1.it/~bibdis/RePEc/aeg/report/2014-17.pdf
    File Function: First version, 2014
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    References listed on IDEAS

    as
    1. Paul Tseng & Sangwoon Yun, 2010. "A coordinate gradient descent method for linearly constrained smooth optimization and support vector machines training," Computational Optimization and Applications, Springer, vol. 47(2), pages 179-206, October.
    2. Giampaolo Liuzzi & Laura Palagi & Mauro Piacentini, 2010. "On the convergence of a Jacobi-type algorithm for Singly Linearly-Constrained Problems Subject to simple Bounds," DIS Technical Reports 2010-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    3. Joachims, Thorsten, 1998. "Making large-scale SVM learning practical," Technical Reports 1998,28, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Support Vector Machines ; Machine Learning ; Parallel Computing ; Decomposition Techniques ; Huge Data;
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