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 |
Support Vector Machine, Optimization, Separating Hyperplane, Sequential Minimal Optimization
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| [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. |
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
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
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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}
}
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 -
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