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Support Vector Machine Classification Methods: A Review and Comparison with Different Classifiers

Ankit P. Vaishnav, Amit P. Ganatra, C.K. Bhensdadia


Support Vector Machines (SVMs) have been extensively researched in the data mining and machinelearning communities for the last decade and actively applied to applications in various domains. SVMs are typically used for learning classification and regression tasks. Two special properties of SVMs are that they achieve (1) high generalization by maximizing the margin and (2) support an efficient learning of nonlinear functions by kernel trick. Many algorithms and their improvements have been proposed to train SVMs. This paper presents a comprehensive description of various SVM methods and compares SVM classifier with other classification methods.


Classifiers, Machine Learning, Predictive Accuracy, Support Vector Machine (SVM)

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V. Vapnik. The Nature of Statistical Learning Theory. Springer, N.Y.,1995. ISBN 0-387-94559-8.

Burges C., “A tutorial on support vector machines for pattern recognition”, In “Data Mining and Knowledge Discovery”. Kluwer Academic Publishers, Boston, 1998, (Volume 2).

V. Vapnik, S. Golowich, and A. Smola. Support vector method for function approximation, regression estimation, and signal processing. In M. Mozer, M. Jordan, and T. Petsche, editors, Advances in Neural Information Processing Systems 9, pp. 281– 287, Cambridge, MA, 1997.MIT Press.

J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, pp. 185-208, 1999.

S. S. Keerthi, S. K. Shevade, C. Bhattacharyya, K. R. K. Murthy Improvements to Platt’s SMO Algorithm for SVM Classifier Design.

Raul Acosta Hernandez, Marius Strum, Wang Jiang Chau and Jose Artur Quilici Gonzalez, The Multiple Pairs SMO: A Modified SMO Algorithm for the Acceleration of the SVM Training, Proceeding of International Joint Conference on Neural Networks, Atlanta, Georgia, USA, June 14-19, 2009.

Peng Peng, Qian-Li Ma, Lei-Ming Hong, The Research Of The Parallel SMO Algorithm For Solving SVM, Proceedings of the Eighth International Conference on Machine Learning and Cybernetics, Baoding, 12-15 July 2009.

S.Shahbudin, A. Hussain, S. A. Samad, N. Md Tahir, Training and Analysis of Support Vector Machine using Sequential Minimal Optimization.

Osuna, E.; Freund, R.; Girosi, F., An improved Training Algorithm for Support Vector Machines, Neural Networks for Signal Processing, Proceedings of the 1997 IEEE Workshop.

Rong-En Fan, Pai-Hsuen Chen, Chih-Jen Lin, Working Set Selection Using Second Order Information for Training Support Vector Machines, Journal of Machine Learning Research (2005) pp.1889–1918.

Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a Library for Support Vector Machines.

Chih-Wei Hsu, Chih-Chung Chang, and Chih-Jen Lin, A Practical Guide to Support Vector Classification.

Alexandros Karatzoglou, Alex Smola, Kurt Hornik , kernlab – An S4 Package for Kernel Methods in R.

Thorsten Joachims, Making Large Scale SVM Learning Practical, MIT Press, Cambridge, USA, 1998.

David Meyer, Support Vector Machines: The Interface to libsvm in package e1071.

Zhao Lihong , Song Ying , Zhu Yushi , Zhang Cheng , Zheng Y, Face Recognition based on multi-class SVM.

Pornpon Thamrongrat, Ladda Preechaveerakul, Wiphada Wettayaprasit, A Novel Voting Algorithm of Multi-Class SVM for Web Page Classification.


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