The convergence rate of AdaBoost

COLT 2010 - The 23rd Conference on Learning Theory

Volumn , Issue , 2010, Pages 308-309

  Schapire, Robert E ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE RATES; RATE OF CONVERGENCE;

ADAPTIVE BOOSTING;

EID: 80052392863     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. Bartlett, P. L., & Traskin, M. (2007). AdaBoost is consistent. Journal of Machine Learning Research, 8, 2347-2368.
2. Bickel, P. J., Ritov, Y., & Zakai, A. (2006). Some theory for generalized boosting algorithms. Journal of Machine Learning Research, 7, 705-732.
3. Boyd, S., & Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
4. Breiman, L. (1999). Prediction games and arcing classifiers. Neural Computation, 11, 1493-1517.
5. Collins, M., and Schapire., R. E., & Singer, Y. (2002). Logistic regression, AdaBoost and Bregman distances. Machine Learning, 48.
6. Frean, M., & Downs, T. (1998). A simple cost function for boosting (Technical Report). Department of Computer Science and Electrical Engineering, University of Queensland.
7. Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
8. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 38, 337-374.
9. Luenberger, D. G., & Ye, Y. (2008). Linear and nonlinear programming. Springer. Third edition.
10. Luo, Z. Q., & Tseng, P. (1992). On the convergence of the coordinate descent method for convex differentiable minimization. Journal of Optimization Theory and Applications, 72, 7-35.
11. Mason, L., Baxter, J., Bartlett, P., & Frean, M. (1999). Functional gradient techniques for combining hypotheses. In Advances in large margin classifiers. MIT Press.
12. Onoda, T., Rätsch, G., & Müller, K.-R. (1998). An asymptotic analysis of AdaBoost in the binary classification case. Proceedings of the 8th International Conference on Artificial Neural Networks (pp. 195-200).
13. Rätsch, G., Mika, S., & Warmuth, M. K. (2002). On the convergence of leveraging. Advances in Neural Information Processing Systems 14.
14. Rätsch, G., Onoda, T., & Müller, K.-R. (2001). Soft margins for AdaBoost. Machine Learning, 42, 287-320.
15. Schapire, R. E., & Singer, Y. (1999). Improved boosting algorithms using confidence-rated predictions. Machine Learning, 37, 297-336.
16. Shalev-Shwartz, S., & Singer, Y. (2008). On the equivalence of weak learnability and linear separability: New relaxations and efficient boosting algorithms. 21st Annual Conference on Learning Theory.
17. Zhang, T., & Yu, B. (2005). Boosting with early stopping: Convergence and consistency. Annals of Statistics, 33, 1538-1579.