Tutorial on Support Vector Machine
Applied and Computational Mathematics
Volume 6, Issue 4-1, July 2017, Pages: 1-15
Received: Sep. 7, 2015; Accepted: Sep. 8, 2015; Published: Jun. 17, 2016
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Author
Loc Nguyen, Sunflower Soft Company, Ho Chi Minh City, Vietnam
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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.
Keywords
Support Vector Machine, Optimization, Separating Hyperplane, Sequential Minimal Optimization
To cite this article
Loc Nguyen, Tutorial on Support Vector Machine, Applied and Computational Mathematics. Special Issue:Some Novel Algorithms for Global Optimization and Relevant Subjects. Vol. 6, No. 4-1, 2017, pp. 1-15. doi: 10.11648/j.acm.s.2017060401.11
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.
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