Enhancing the Accuracy 0 F Svm Classifiers with Kernel and Parameter Training

  title={Enhancing the Accuracy 0 F Svm Classifiers with Kernel and Parameter Training},
  author={D. UDHAYAKUMARAPANDIAN and Rm. Chandrasekaran and Ammasai Kumaravel},
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… CONTINUE READING
0 Extracted Citations
15 Extracted References
Similar Papers

Referenced Papers

Publications referenced by this paper.
Showing 1-10 of 15 references

Gunn . “ Support Vector Machines for Classification and Regression Technical Report ”

  • R. Steve
  • Faculty of Engineering , Science and Mathematics…
  • 2014

RandomForests,”inMachine Learning, vol

  • L. Breiman
  • 45, pp. 5-32,
  • 2001

A comparison of dynamic reposing and tangent distance for drug activity prediction

  • T. G. Jain. Dietterich., A. Lathrop., R. Lozano-Perez
  • Advances in Neural Information Processing Systems
  • 1998

Seem. Ascospore release and infection of apple leaves by conidia and ascospores of Venturia inaequalis at low temperatures

  • A.Stensvand, T. Amundsen, L. Semb, D. M. Gadoury, R.C
  • Phytopathology
  • 1997

Similar Papers

Loading similar papers…