| Peer-Reviewed

Tutorial on Support Vector Machine

Received: 7 September 2015     Accepted: 8 September 2015     Published: 17 June 2016
Views:       Downloads:
Abstract

Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.

Published in Applied and Computational Mathematics (Volume 6, Issue 4-1)

This article belongs to the Special Issue Some Novel Algorithms for Global Optimization and Relevant Subjects

DOI 10.11648/j.acm.s.2017060401.11
Page(s) 1-15
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Support Vector Machine, Optimization, Separating Hyperplane, Sequential Minimal Optimization

References
[1] M. Law, "A Simple Introduction to Support Vector Machines," 2006.
[2] Wikibooks, "Support Vector Machines," Wikimedia Foundation, 1 January 2008. [Online]. Available: http://en.wikibooks.org/wiki/Support_Vector_Machines. [Accessed 2008].
[3] V. G. Honavar, "Sequential Minimal Optimization for SVM," Vasant Honavar homepage, Ames, Iowa, USA.
[4] S. Boyd and L. Vandenberghe, Convex Optimization, New York, NY: Cambridge University Press, 2009, p. 716.
[5] Wikipedia, "Karush–Kuhn–Tucker conditions," Wikimedia Foundation, 4 August 2014. [Online]. Available: http://en.wikipedia.org/wiki/Karush–Kuhn–Tucker_conditions. [Accessed 16 November 2014].
[6] Y.-B. Jia, "Lagrange Multipliers," 2013.
[7] J. C. Platt, "Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines," Microsoft Research, 1998.
[8] A. W. Moore, "Support Vector Machines," Available at http://www. cs. cmu. edu/~awm/tutorials, 2001.
[9] I. Johansen, Graph software, GNU General Public License, 2012. G. Eason, B. Noble, and I. N. Sneddon, “On certain integrals of Lipschitz-Hankel type involving products of Bessel functions,” Phil. Trans. Roy. Soc. London, vol. A247, pp. 529–551, April 1955. (References).
[10] N. Cristianini, "Support Vector and Kernel Machines," in The 28th International Conference on Machine Learning (ICML), Bellevue, Washington, USA, 2001.
Cite This Article
  • APA Style

    Loc Nguyen. (2016). Tutorial on Support Vector Machine. Applied and Computational Mathematics, 6(4-1), 1-15. https://doi.org/10.11648/j.acm.s.2017060401.11

    Copy | Download

    ACS Style

    Loc Nguyen. Tutorial on Support Vector Machine. Appl. Comput. Math. 2016, 6(4-1), 1-15. doi: 10.11648/j.acm.s.2017060401.11

    Copy | Download

    AMA Style

    Loc Nguyen. Tutorial on Support Vector Machine. Appl Comput Math. 2016;6(4-1):1-15. doi: 10.11648/j.acm.s.2017060401.11

    Copy | Download

  • @article{10.11648/j.acm.s.2017060401.11,
      author = {Loc Nguyen},
      title = {Tutorial on Support Vector Machine},
      journal = {Applied and Computational Mathematics},
      volume = {6},
      number = {4-1},
      pages = {1-15},
      doi = {10.11648/j.acm.s.2017060401.11},
      url = {https://doi.org/10.11648/j.acm.s.2017060401.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.acm.s.2017060401.11},
      abstract = {Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.},
     year = {2016}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Tutorial on Support Vector Machine
    AU  - Loc Nguyen
    Y1  - 2016/06/17
    PY  - 2016
    N1  - https://doi.org/10.11648/j.acm.s.2017060401.11
    DO  - 10.11648/j.acm.s.2017060401.11
    T2  - Applied and Computational Mathematics
    JF  - Applied and Computational Mathematics
    JO  - Applied and Computational Mathematics
    SP  - 1
    EP  - 15
    PB  - Science Publishing Group
    SN  - 2328-5613
    UR  - https://doi.org/10.11648/j.acm.s.2017060401.11
    AB  - Support vector machine is a powerful machine learning method in data classification. Using it for applied researches is easy but comprehending it for further development requires a lot of efforts. This report is a tutorial on support vector machine with full of mathematical proofs and example, which help researchers to understand it by the fastest way from theory to practice. The report focuses on theory of optimization which is the base of support vector machine.
    VL  - 6
    IS  - 4-1
    ER  - 

    Copy | Download

Author Information
  • Sunflower Soft Company, Ho Chi Minh City, Vietnam

  • Sections