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ISRN Signal Processing
Volume 2012 (2012), Article ID 740761, 8 pages
http://dx.doi.org/10.5402/2012/740761
Research Article

Online Boosting Algorithm Based on Two-Phase SVM Training

1Department of Information Processing, Tokyo Institute of Technology, Tokyo 152-8550, Japan
2Imaging Science and Engineering Laboratory, Tokyo Institute of Technology, Tokyo 152-8550, Japan

Received 18 May 2012; Accepted 25 June 2012

Academic Editors: G. Camps-Valls and B. Yuan

Copyright © 2012 Vsevolod Yugov and Itsuo Kumazawa. 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. H. Grabner, M. Grabner, and H. Bischof, “Real-time tracking via on-line boosting,” in Proceedings of the British Machine Vision Conference (BMVC '06), vol. 1, pp. 47–56, September 2006.
  2. N. Oza and S. Russell, “Online bagging and boosting,” in Artificial Intelligence and Statistics Pages, pp. 105–112, Morgan Kaufmann, Boston, Mass, USA, 2001.
  3. J. Kivinen, A. J. Smola, and R. C. Williamson, “Online learning with kernels,” IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2165–2176, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Shalev-Shwartz, Y. Singer, and N. Srebro, “Pegasos: primal estimated sub-GrAdient sOlver for SVM,” in Proceedings of the 24th ACM International Conference on Machine Learning (ICML '07), pp. 807–814, June 2007. View at Publisher · View at Google Scholar · View at Scopus
  5. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995.
  6. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, New York, NY, USA, 2004.
  7. J. C. Platt, “Fast training of support vector machines using sequential minimal optimization,” in Advances in Kernel Methods, pp. 185–208, MIT Press, Cambridge, Mass, USA, 1999.
  8. Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119–139, 1997. View at Scopus
  9. R. E. Schapire, Y. Freund, P. Bartlett, and W. S. Lee, “Boosting the margin: a new explanation for the effectiveness of voting methods,” Annals of Statistics, vol. 26, no. 5, pp. 1651–1686, 1998. View at Scopus
  10. G. Ratsch, B. Scholkopf, S. Mika, and K.-R. Muller, “SVM and boosting: one class,” Tech. Rep., 2000.
  11. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Scopus
  12. J. A. Blackard, “Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables,” Computers and Electronics in Agriculture, vol. 24, no. 3, pp. 131–151, 1999. View at Publisher · View at Google Scholar · View at Scopus