Table of Contents
ISRN Applied Mathematics
Volume 2013, Article ID 520635, 11 pages
http://dx.doi.org/10.1155/2013/520635
Research Article

A Faster Gradient Ascent Learning Algorithm for Nonlinear SVM

1Department of Informatics and Cybernetics, Bucharest University of Economic Studies, Dorobanţi 15-17, 010552 Bucharest, Romania
2Department of Mathematics and Informatics, University of Pitesti, Târgu din Vale No. 1, 110040 Pitesti, Romania
3Department of Informatics, Ionian University, Palaia Anaktora 49100 Corfu, Greece

Received 27 May 2013; Accepted 19 July 2013

Academic Editors: S. He and L. Wu

Copyright © 2013 Catalina-Lucia Cocianu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. V. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995. View at Zentralblatt MATH · View at MathSciNet
  2. V. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998. View at Zentralblatt MATH · View at MathSciNet
  3. S. Abe, “Support vector machines for pattern classification,” in Advances in Pattern Recognition, Springer, London, UK, 2010. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  4. J. Shawe-Taylor and N. Cristianini, Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge, UK, 2000.
  5. E. Osuna, R. Freund, and F. Girosi, “Improved training algorithm for support vector machines,” in Proceedings of the 7th IEEE Workshop on Neural Networks for Signal Processing (NNSP '97), pp. 276–285, September 1997. View at Scopus
  6. L. I.-J. Chien, C.-C. Chang, and Y.-J. Lee, “Variant methods of reduced set selection for reduced support vector machines,” Journal of Information Science and Engineering, vol. 26, no. 1, pp. 183–196, 2010. View at Google Scholar · View at Scopus
  7. Y.-J. Lee and O. L. Mangasarian, “SSVM: a smooth support vector machine for classification,” Computational Optimization and Applications, vol. 20, no. 1, pp. 5–22, 2001. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  8. L. J. Cao, S. S. Keerthi, C. J. Ong, P. Uvaraj, X. J. Fu, and H. P. Lee, “Developing parallel sequential minimal optimization for fast training support vector machine,” Neurocomputing, vol. 70, no. 1–3, pp. 93–104, 2006. View at Publisher · View at Google Scholar · View at Scopus
  9. G. C. Cawley and N. L. C. Talbot, “Improved sparse least-squares support vector machines,” Neurocomputing, vol. 48, pp. 1025–1031, 2002. View at Publisher · View at Google Scholar · View at Scopus
  10. C.-H. Li, H.-H. Ho, Y.-L. Liu, C.-T. Lin, B.-C. Kuo, and J.-S. Taur, “An automatic method for selecting the parameter of the normalized kernel function to support vector machines,” Journal of Information Science and Engineering, vol. 28, no. 1, pp. 1–15, 2012. View at Google Scholar · View at MathSciNet · View at Scopus
  11. T. Joachims, “Making large-scale SVM learning practical,” in Advances in Kernel Methods—Support Vector Learning, pp. 41–56, 1998. View at Google Scholar
  12. J. A. K. Suykens, J. de Brabanter, L. Lukas, and J. Vandewalle, “Weighted least squares support vector machines: robustness and sparce approximation,” Neurocomputing, vol. 48, pp. 85–105, 2002. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Rueping, “mySVM: another one of those support vector machines,” 2003, http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM.
  14. E. Alpaydin, Introduction to Machine Learning, MIT Press, Cambridge, Mass, USA, 2004.
  15. P. Laskov, “Feasible direction decomposition algorithms for training support vector machines,” Machine Learning, vol. 46, no. 1–3, pp. 315–349, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. S. Shalev-Shwartz, Y. Singer, and N. Srebro, “Pegasos: primal estimated sub-GrAdient sOlver for SVM,” in Proceedings of the 24th International Conference on Machine Learning (ICML '07), pp. 807–814, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. V. Yugov and I. Kumazava, “Online boosting algorithm based on two-phase SVM training,” ISRN Signal Processing, vol. 12, 2012. View at Google Scholar
  18. L. State, C. Cocianu, and M. Mircea, “Heuristic attempts to improve the generalization capacities in learning SVMs,” in Proceedings of the 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, pp. 108–116, 2012.
  19. C.-L. Cocianu, L. State, and P. Vlamos, “A new method for learning the support vector machines,” in Proceedings of the 6th International Conference on Software and Database Technologies (ICSOFT '11), pp. 365–370, July 2011. View at Scopus
  20. http://archive.ics.uci.edu/ml/index.html.