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Identifying Efficient Kernel Function in Multiclass Support Vector Machines
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 28 - Number 8 |
| Year of Publication: 2011 |
| Authors: R.Sangeetha, Dr.B.Kalpana |
10.5120/3408-4754
|
R.Sangeetha, Dr.B.Kalpana . Identifying Efficient Kernel Function in Multiclass Support Vector Machines. International Journal of Computer Applications. 28, 8 ( August 2011), 18-23. DOI=10.5120/3408-4754
Abstract
Support vector machine (SVM) is a kernel based novel pattern classification method that is significant in many areas like data mining and machine learning. A unique strength is the use of kernel function to map the data into a higher dimensional feature space. In training SVM, kernels and its parameters have very vital role for classification accuracy. Therefore, a suitable kernel design and its parameters should be used for SVM training. In this paper, we present certain kernel functions for multiclass support vector machines and propose the appropriate and optimal kernel for one-versus-one (OAO) and one-versus-all (OAA) multiclass support vector machines. The performance of the one-versus-one and one-versus-all multiclass SVM are illustrated by empirical results and it is evaluated by the parameters like support vectors, support vector percentage, classification error, training error and CPU time. The experimental results demonstrate the ability to use more generalized kernel function and it goes to prove that the polynomial kernel’s efficiency in terms of high classification accuracy for several datasets.
References
- Han,J., Kamber,M.2006. Data Mining—Concepts and Technique, 2nd ed. San Mateo, CA: Morgan Kaufmann.
- Tan,P.N.,Steinbach,M. and Kumar,V.2005. Introduction to Data Mining. Reading, MA: Addison-Wesley.
- Vapnik, V. 1999. An overview of statistical learning theory. IEEE Trans. on Neural Networks.
- Cristianini, N. and Shawe-Taylor, J. 2000. Introduction to Support Vector Machines. Cambridge University Press.
- Schölkopf, B. and Smola, A.2001. Leaning with Kernels. MIT Press.
- Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 56–89.
- Corinna Cortes and Vapnik, V. 1995. Support-Vector Networks, Machine Learning.
- Manikandan , J. and Venkataramani, B. 2010.Study and evaluation of a multi-class SVM classifier using diminishing learning technique, Neurocomputing , doi:10.1016/j.neucom.2009.11.042
- Anna Wang, Wenjing Yuan, Junfang Liu, Zhiguo Yu, Hua Li, 2009. A novel pattern recognition algorithm: Combining ART network with SVM to reconstruct a multi-class classifier, Computers and Mathematics with Applications, 1908_1914.
- Vojtech Franc, Václav Hlavá. 2009. Statistical Pattern Recognition Toolbox for Matlab.
- Ralf Herbrich December 2001.Learning kernel classifiers: theory and algorithms, MIT Press, Cambridge, Mass, ISBN 026208306X.
- Sangeetha, R., Kalpana, B. 2010. A comparative study and choice of an appropriate kernel for support vector machines. In: Das, V.V., Vijaykumar, R. (eds.) ICT 2010. CCIS, vol. 101,pp. 549–553. Springer, Heidelberg (2010)
- Sangeetha, R., Kalpana, B.2010. Optimizing the Kernel Selection for Support Vector Machines using Performance Measures. In: A2CWiC 2010, ISBN: 978-1-4503-0194-7.
- Smits ,G.F. and Jordaan, E.M. 2002. Improved SVM Regression using Mixtures of Kernels, IJCNN '02. Proceedings of the International Joint Conference on Neural Networks.
- Weston, J. and Watkins, C. Multi class support vector machines, Technical Report.
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