Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
권호 표지

The Efficiency of Boosting on SVM

  • Published : 2002.10.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we introduce SVM(support vector machine) developed to solve the problem of generalization of neural networks. We also introduce boosting algorithm which is a general method to improve accuracy of some given learning algorithm. We propose a new algorithm combining SVM and boosting to solve classification problem. Through the experiment with real and simulated data sets, we can obtain better performance of the proposed algorithm.

Keywords

References

  1. Data Mining and Knowledge discovery v.2 A Tutorial on Support Vector Machines for Pattern Recognition C. Burges
  2. An Introduction to Support Vector Machines N. Cristianini;J. Shawe-Taylor
  3. Journal of Computer and System Science v.55 no.1 A Decision-Theoretic generalization of on-line learning and an application to boosting Y. Freund;R. E. Schapire
  4. Proceeding of the Sixteenth International Joint Conference on Artificial Intelligence A Brief Introduction to Boosting Y. Freund;R. E. Schapire
  5. ISIS Technical Report, U. of Southampton Support Vector Machines for Classification and Regression S. Gunn
  6. MIT AI Lab., Technical Report Support Vector Machines : Training and Applications E. Osunam;R. Freund;F. Girosi
  7. Technical Report, NeuroCOLT A Tutorial in Support Vector Regression, NeuroCOLT2 A. J. Smola;B. Scholkopf
  8. Communication of the ACM v.27 A Theory of the Learnable L. G. Valiant
  9. Statistical Learning Theory V. Vapnik
  10. Statistical Learning Theory V. Vapnik
  11. Advanced in Large Margin Classifiers Generalized Approximate Cross Validation for Support Vector Machines, or, Another way to Look at Margin-Like Quantities, TR 1006, April 1999. Expanded version of TR1006 posted here February 1999. (With revisions) Wahba, G.;Lin, Y.;Zhang, H.;Smola, Bartlee, Sholkopf(ed.);Schurmans(ed.)