News Updates Friday 21st Oct 2016 :
  • Welcome to International Press Corporation, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of Sept/Oct 2016 issue is 25 Oct 2016, Submit online or at
  • Our journals are indexed in University of Regensburg Germany, Google Scholar, Cross Reference data bases
  • Applications for reviewers are invited and can be sent directly to concerned editor's mail

A Genetic Algorithm Approach to Kernel Functions Parameters Selection for SVM

Author : Kavita Aneja , Saroja and Jyoti

Pages : 713-716
Download PDF

The Support Vector Machines (SVM) is a classification algorithm with many diverse applications. The SVM has many parameters associated with it which influences the performance of the SVM classifier. In this paper, we employ Genetic Algorithm based approach to find and select an appropriate kernel function and its parameters. This proposed technique combines predictive accuracy and complexity of SVM as two criteria into a fitness function for evaluating the performance of SVM. Our method is compared with grid algorithm and the experimental results validate that the proposed approach is much better than the grid method.

Keywords: Support Vector Machines, kernel function, linear kernel, RBF kernel, parameters selection, genetic algorithm

Article published in International Journal of Current  Engineering  and Technology, Vol.3,No.2 (June- 2013)






Call for Papers
  1. IJCET- Sept/Oct-2016 Issue

    Submission Last Date
    25 Oct 2016
  2. IJTT-Sept-2016
  3. IJAIE-Sept-2016
  4. IJCSB-Sept-2016
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2016 INPRESSCO® All Rights Reserved