Mathematical and Quantitative Methods
Abstract
Support Vector Machines (SVMs)have found many applications in various fields. They
have been introduced for classification problems and extended to regression. In this paperI review the
utilization of SVM for classification problems and exemplify this with application on IRIS datasets. I
used the Matlab programming language to implement linear and nonlinear classificators and apply
this on the dataset.
References
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Cambridge University Press.
Cortes, C. & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), pp. 273-297.
Drucker, H.; Burges, C.; Kaufman, L.; Smola, A. & Vapnik, V. (1996). Support vector regression machine.
Advances in Neural Information Processing Systems, Cambridge: MIT Press 9(9): 155–61.
Guggenberger, A. (2008). Another Introduction to Support Vector Machines, Available online at
http://mindthegap.googlecode.com/files/AnotherIntroductionSVM.pdf, accessed May 2012.
Gunn, S. R. (1998). Support Vector Machines for Classification and Regression, University of
Southampton, Available online at http://www.svms.org/tutorials/Gunn1998.pdf, accessed April 2012.
Lovell, B. C.; Walder, C. J. (2006). Support Vector Machines for Business Applications. Business
Applications and Computational Intelligence. Hershey, U.S.A: Idea Group, pp. 267-290.
Minoux, M. (1986). Mathematical Programming: Theory and Algorithms. John Wiley and Sons.
Smola, J. (1996). Regression estimation with support vector learning machine. Master’s thesis. Munchen:
Technische Universitat Munchen.
Smola, A.; Schölkopf, B. (1998). On a Kernel-based Method for Pattern Recognition, Regression,
Approximation and Operator Inversion. GMD Technical Report No. 1064.
Shu-Xia Lu, X. -Z. W. (2004). A comparison among four SVM classification methods: Lsvm, nlsvm, ssvm
and nsvm. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 7, pp.
4277–4282, Shanghai, China.
Vapnik, V. (2006). Empirical Inference Science. Afterword in 1982 reprint of Estimation of Dependences
Based on Empirical Data.
function method in pattern recognition learning. Automation and Remote Control, Vol. 25, pp. 821–837.
Bellman, R. E. (1961). Adaptive Control Processes. Princeton, NJ: Princeton University Press.
Bennett, K. P.; Campbell, C. (2000). Support vector machines: hype or hallelujah? SIGKDD Explorations
Newsl., Vol 2, No. 2, pp. 1–13.
Boser, B.; Guyon, I.; Vapnik, V. (1992). A training algorithm for optimal margin classifiers. Proceedings of
the 5th Annual Workshop on Computational Learning Theory, pp. 144–52.
Blanz, V.; Schölkopf, B.; Bülthoff, H.; Burges, C.; Vapnik, V. & Vetter, T. (1996). Comparison of viewbased
object recognition algorithms using realistic 3D models, In: C. von der Malsburg, W. von Seelen, J.
C. Vorbrüggen, and B. Sendhoff (eds.): Artificial Neural Networks - ICANN’96. Springer Lecture Notes in
Computer Science Vol. 1112, Berlin, pp. 251-256.
Chen, S.; Jeong, K.; Härdle, W. (2008). Support Vector Regression Based GARCH Model with Application
to Forecasting Volatility of Financial Returns, SFB 649 "Economic Risk", Humboldt-Universität zu Berlin,
Berlin. Available online at http://edoc.hu-berlin.de/series/sfb-649-papers/2008-14/PDF/14.pdf.
Christiani, N. & Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines. Cambridge:
Cambridge University Press.
Cortes, C. & Vapnik, V. (1995). Support vector networks. Machine Learning, 20(3), pp. 273-297.
Drucker, H.; Burges, C.; Kaufman, L.; Smola, A. & Vapnik, V. (1996). Support vector regression machine.
Advances in Neural Information Processing Systems, Cambridge: MIT Press 9(9): 155–61.
Guggenberger, A. (2008). Another Introduction to Support Vector Machines, Available online at
http://mindthegap.googlecode.com/files/AnotherIntroductionSVM.pdf, accessed May 2012.
Gunn, S. R. (1998). Support Vector Machines for Classification and Regression, University of
Southampton, Available online at http://www.svms.org/tutorials/Gunn1998.pdf, accessed April 2012.
Lovell, B. C.; Walder, C. J. (2006). Support Vector Machines for Business Applications. Business
Applications and Computational Intelligence. Hershey, U.S.A: Idea Group, pp. 267-290.
Minoux, M. (1986). Mathematical Programming: Theory and Algorithms. John Wiley and Sons.
Smola, J. (1996). Regression estimation with support vector learning machine. Master’s thesis. Munchen:
Technische Universitat Munchen.
Smola, A.; Schölkopf, B. (1998). On a Kernel-based Method for Pattern Recognition, Regression,
Approximation and Operator Inversion. GMD Technical Report No. 1064.
Shu-Xia Lu, X. -Z. W. (2004). A comparison among four SVM classification methods: Lsvm, nlsvm, ssvm
and nsvm. Proceedings of 2004 International Conference on Machine Learning and Cybernetics, vol. 7, pp.
4277–4282, Shanghai, China.
Vapnik, V. (2006). Empirical Inference Science. Afterword in 1982 reprint of Estimation of Dependences
Based on Empirical Data.
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Published
2021-05-26
How to Cite
Collective Authors. (2021). Mathematical and Quantitative Methods: Array. Acta Universitatis Danubius. Œconomica, 8(5). Retrieved from https://dj.univ-danubius.ro/index.php/AUDOE/article/view/1065
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