ENHANCING THE ACCURACY 0F SVM CLASSIFIERS WITH KERNEL AND PARAMETER TRAINING

Authors

  • D.UDHAYAKUMARAPANDIAN Research Scholar, Department of Computer Science and Engineering. Annamalai University, Chidambaram-608 002, India
  • RM. CHANDRASEKARAN Professor, Department of Computer Science and Engineering. Annamalai University, Chidambaram-608 002, India,
  • A.KUMARAVEL Professor and Dean, Department of Computer Science and Engineering.Bharath University, Selaiyur, Chennai-600073, India

Keywords:

Support Vector Machine, Kernel functions, Polynomial kernel, Normalized polynomial, Pearson VII function-based, RBF kernel.

Abstract

Data mining methods based on support vector machine are attractive to address the curse of dimensionality. The Kernel mapping contributes a unifying frame work for most of the commonly employed models to get the linear planes in the higher dimensional space. In this paper, we prove this approach enhances the accuracy  of diabetes data set. We further refine the results with parameter tuning for the selected kernels. The natural question that arises in case of many such different mappings to choose from, which is the best for a particular problem? The selection can be validated using independent test sets or variety of data sets and methods of cross validations.

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Published

30.06.2015

How to Cite

D.UDHAYAKUMARAPANDIAN, RM. CHANDRASEKARAN, & A.KUMARAVEL. (2015). ENHANCING THE ACCURACY 0F SVM CLASSIFIERS WITH KERNEL AND PARAMETER TRAINING. International Journal of Pharma and Bio Sciences, 6(2), 204–217. Retrieved from https://ijpbs.net/index.php/journal/article/view/4218

Issue

Volume 6, Issue 2, 2015

Section

Review Articles

Categories

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