Different Paradigms for Choosing Sequential Reweighting Algorithms

Neural Computation

Volumn 16, Issue 4, 2004, Pages 811-836

  Blanchard, Gilles ^a,b  

^a UNIVERSITÉ PARIS SUD   (France)

^b FRAUNHOFER FIRST   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 1542351222     PISSN: 08997667     EISSN: None     Source Type: Journal    
DOI: 10.1162/089976604322860712     Document Type: Review

References (26)

1. Amit, Y., & Blanchard, G. (2001). Multiple randomized classifiers: MRCL (Tech. Rep.). Chicago: University of Chicago. Available on-line: http://galton.uchicago.edu/~amit/Papers/mrcl.ps.gz.
2. Amit, Y., & Geman, D. (1997). Shape quantization and recognition with randomized trees. Neural Computation, 9, 1545-1588.
3. Blackwell, D. (1956). An analog of the minmax theorem for vector payoffs. Pacific Journal of Mathematics, 6, 1-8.
4. Blanchard, G. (2001). Mixture and aggregation of estimators for pattern recognition: Application to decision trees. Unpublished doctoral dissertation, Université Paris-Nord. (In English, with an introductory part in French) Available online: http://www.math.u-psud.fr/~blanchard/publi/these.ps.gz.
5. Blanchard, G. (2003). Generalization error bounds for aggregate classifiers. In D. D. Denison, M. H. Hansen, C. C. Holmes, B. Mallick, & B. Yu (Eds.), Nonlinear estimation and classification. Berlin: Springer-Verlag.
6. Breiman, L. (1998a). Arcing classifiers. Annals of Statistics, 26(3), 801-849.
7. Breiman, L. (1998b). Prediction games and arcing algorithms (Tech. Rep.). Berkeley Statistics Department, University of California at Berkeley. Available on-line: ftp://ftp.stat.berkeley.edu/pub/users/breiman/games.ps.Z.
8. Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
9. Devroye, L., Györfi, L., & Lugosi, G. (1996). A probabilistic theory of pattern recognition. Berlin: Springer-Verlag.
10. Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization. Machine Learning, 40, 139-158.
11. Frean, M., & Downs, T. (1998). A simple cost function for boosting. (Tech. Rep.). Queensland, Australia: Department of Computer Science and Electrical Engineering, University of Queensland. Available on-line: http://www.boosting.org/papers/FreDow98.ps.gz.
12. Freund, Y., & Schapire, R. (1996). Experiments on a new Boosting algorithm. In Machine Learning: Proceedings of the 13th International Conference (pp. 148-156). San Mateo, CA: Morgan Kaufmann.
13. Freund, Y., & Schapire, R. E. (1996). Game theory, on-line prediction and boosting. In Proceedings of the 9th Annual Conference on Computational Learning Theory (pp. 325-332). New York: ACM Press.
14. Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28, 337-374.
15. Grove, A., & Schuurmans, D. (1998). Boosting in the limit: Maximizing the margin of learned ensembles, in Proceedings of the Fifteenth National Conference on Artifical Intelligence (pp. 692-699). New York: AAAI Press. Available online: http://www.boosting.org/papers/GroSch98.ps.gz.
16. Hart, S., & Mas-Colell, A. (2001). A general class of adaptive strategies. Journal of Economic Theory, 98, 26-54.
17. Koltchinkskii, V., & Panchenko, D. (2002). Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics, 30(1), 1-35.
18. Meir, R., & Rätsch, G. (2003). An introduction to Boosting and leveraging. In S. Mendelson & A. Smola (Eds.), Advanced lectures on machine learning (pp. 119-184) Berlin: Springer-Verlag. Available on-line: http://www.boosting.org/papers/MeiRae03.ps.gz.
19. Mika, S. (2002). Kernel Fisher discriminants. Unpublished doctoral dissertation, University of Technology, Berlin.
20. Onoda, T., Rätsch, G., & Müller, K.-R. (1998). An asymptotic analysis of AdaBoost in the binary classification case. In L. Niklasson, M. Bodén, & T. Ziemke (Eds.), Proc. of the Int. Conf. on Artificial Neural Networks (ICANN'98) (pp. 195-200). Berlin: Springer-Verlag. Available on-line: http://www.boosting.org/papers/ICANN98.ps.gz.
21. Rätsch, G., Mika, S., Schlkopf, B., & Müller, K.-R. (2002). Constructing Boosting algorithms from SVMs: An application to one-class classification. IEEE P.A.M.I., 24(9), 1184-1199.
22. Rätsch, G., Onoda, T., & Müller, K.-R. (2001). Soft margins for AdaBoost. Machine Learning, 3(42), 287-320.
23. Rätsch, G., & Warmuth, M. (2001). Marginal boosting (Tech. Rep.). London: Royal Holloway College.
24. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5), 1651-1686.
25. Smola, A. J., Bartlett, P. L., Schölkopf, B., & Schuurmans, D. (Eds.). (2000). Advances in large margin classifiers. Cambridge, MA: MIT Press.
26. Viola, P., & Jones, M. J. (2001). Robust real-time object detection (Tech. Rep. CRL2001/01). Cambridge, MA: COMPAQ.