Empirical Bernstein boosting

Journal of Machine Learning Research

Volumn 9, Issue , 2010, Pages 733-740

  Shivaswamy, Pannagadatta K ^a   Jebara, Tony ^a  

^a Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BOOSTING ALGORITHM; CONCENTRATION INEQUALITY; DATA SETS; EMPIRICAL RISK MINIMIZATION; HOEFFDING'S INEQUALITY; PERFORMANCE GAIN; SAMPLE VARIANCE; STATE OF THE ART; VARIANCE INFORMATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; SUPPORT VECTOR MACHINES;

ADAPTIVE BOOSTING;

EID: 84862162016     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Conference Paper

References (14)

1. Audibert, J.-Y., Munos, R., & Szepesvári, C. (2007). Tuning bandit algorithms in stochastic environments. 18th Algorithmic Learning Theory (pp. 150-165).
2. Bartlett, P. L., Jordan, M. I., & McAuliffe, J. D. (2006). Convexity, classification, and risk bounds. Journal of the American Statistical Association, 101, 138-156.
3. Cesa-Bianchi, N., Conconi, A., & Gentile, C. (2002). A second-order perceptron algorithm. in Proc. 5th Annu. Conf. Computational Learning Theory (Lecture Notes in Artificial Intelligence) (pp. 121-137).
4. Crammer, K., Dredze, M., & Pereira, F. (2009a). Exact convex confidence-weighted learning. Advances in Neural Information Processing Systems 21. Cambridge, MA: MIT Press.
5. Crammer, K., Mohri, M., & Pereira, F. (2009b). Gaussian margin machines. Proceedings of the Artificial Intelligence and Statistics.
6. Freund, Y., & Schapire, R. E. (1997). A decisiontheoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119-139.
7. Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: data mining, inference, and prediction. New York: Springer- Verlag.
8. Koltchinskii, V., & Panchenko, D. (2002). Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics, 30.
9. Maurer, A., & Pontil, M. (2009). Empirical Bernstein bounds and sample variance penalization. 22nd Annual Conference on Learning Theory.
10. Mnih, V., Szepesvári, C., & Audibert, J.-Y. (2008). Empirical Bernstein stopping. Proceedings of the Twenty-Fifth International Conference on Machine Learning (pp. 672-679).
11. Raetsch, G., Onoda, T., & Muller, K.-R. (2001). Soft margins for adaboost. Machine Learning, 43, 287-320.
12. Schapire, R. E., Freund, Y., Bartlett, P., & Lee, W. S. (1998). Boosting the margin: a new explanation for the effectiveness of voting methods. The Annals of Statistics, 26, 322-330.
13. Shivaswamy, P., & Jebara, T. (2010). Maximum relative margin and data-dependent regularization. Journal of Machine Learning Research, 11, 747-788.
14. Vapnik, V. (1995). The nature of statistical learning. New York, NY: Springer.