![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_open_medium.gif)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltext_restricted_medium.gif)
A Review on Support Vector Machines for Classification Problems
Subscribe/Renew Journal
Support Vector Machine (SVM) is one of the best techniques to classify the data into multiple classes. In the recent years, support vector machine has been used extensively for classification problems. The main advantage of using support vector machines is its better generalization ability while using higher dimension of data. This paper gives a review on the formulation of some important variants of SVM i.e. hard margin SVM, soft margin SVM, Least Squares Support Vector Machine (LSSVM), Twin Support Vector Machine (TWSVM) and Least Squares Support Vector Machine (LS-TWSVM). To check the effectiveness of these methods, numerical experiments are performed on artificial and real world datasets.
Keywords
Support Vector Machine (SVM), Twin Support Vector Machine (TWSVM), Least Squares method, Artificial Neural Network (ANN).
User
Subscription
Login to verify subscription
Font Size
Information
- C., Cortes, & V. Vapnik. Support-Vector Networks, Machine Learning, 20(3), pp.273-297 (1995).
- C.J.C., Burges. Geometry and invariance in kernel based methods, In Advances in Kernel Methods-Support Vector Learning, Bernhard Scholkopf, Cristopher J.C. Burges and Alexander J. Smola (eds.), MIT Press, Cambridge, MA (1998).
- D. Tomar, D. Ojha, and S. Agarwal. “An Emotion Detection System Based on Multi Least Squares Twin Support Vector Machine.” Advances in Artificial Intelligence (2014).
- D. Tomar and S. Agarwal, “Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes,” Advances in Artificial Neural Systems, vol. 2015, Article ID 265637, 10 pages, 2015.
- E. Borovikov,”An Evaluation of Support Vector Machines as a Pattern Recognition Tool”, http://www.umiacs.umd.edu/users/yab/SVMForPatternRecognition/report.pdf, University of Maryland at College Park (2005).
- E. Osuna, R. Freund and F. Girosi. Training support vector machines: an application to face detection. In Computer vision and pattern recognition, 1997. Proceedings. 1997 IEEE computer society conference on (pp. 130-136) (1997). IEEE.
- Jayadeva, R. Khemchandani, and S. Chandra. Twin support vector machines for pattern classification, IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), vol. 29, pp. 905-910 (2007).
- J. A. K. Suykens and J. Vandewalle, “Least squares support vector machine classifiers,” Neural Processing Letters, Vol. 9, pp. 293-300 (1999).
- J. Zhang and Y. Liu, “Cervical Cancer Detection Using SVM-Based Feature Screening,” Proc. Seventh Int’l Conf. Medical Image Computing and Computer Aided Intervention, pp. 873-880 (Oct. 2004).
- L. Khan, M. Awad, B. Thuraisingham. A new intrusion detection system using support vector machines and hierarchical clustering. The VLDB Journal, vol. 16, pp. 507–521 (2007).
- M.A. Kumar, M. Gopal, Least squares twin support vector machines for pattern classification, Expert Syst. Appl. vol. 36 (4), 7535–7543 (2009).
- M. Schmidt and H. Gish, "Speaker identification via support vector classifiers," Acoustics, Speech, and Signal Processing, 1996. ICASSP-96. Conference Proceedings, 1996 IEEE International Conference on, Atlanta, GA, vol. 1, pp. 105-108 (1996).
- N. Cristianini and J. S. Taylor. An Introduction to Support Vector Machines: And Other Kernel-Based Learning Methods. Cambridge University Press, New York, NY, USA (1999).
- P.J. Phillips, “Support Vector Machines Applied to Face Recognition,” Proc. Conf. Advances in Neural Information Processing Systems. vol. 11, pp. 803-809 (1998).
- P. Michel, R. el Kaliouby. Real time facial expression recognition in video using support vector machines. In: Proceedings of the 5th International Conference on Multimodal Interfaces, pp. 258–264, ISBN: 1-58113-621-8 (2003).
- P.M, Murphy, & D.W. Aha,. UCI repository of machine learning databases, University of California, Irvine. http://www.ics.uci.edu/~mlearn (1992).
- S. R., Gunn, "Support vector machines for classification and regression." ISIS technical report 14 (1998).
- Training support vector machines: An application to face detection, in Proceedings of Computer Vision and Pattern Recognition, pp. 130-136.
- V.N., Vapnik. Statistical Learning Theory, John Wiley & Sons, New York (1998).
- V.N., Vapnik. The nature of statistical learning theory, 2nd ed., Springer, New York (2000).
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 302
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 5