Deep boosting

31st International Conference on Machine Learning, ICML 2014

Volumn 4, Issue , 2014, Pages 2917-2930

  Cortes, Corinna ^a   Mohri, Mehryar ^a   Syed, Umar ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; ARTIFICIAL INTELLIGENCE; DECISION TREES; LEARNING SYSTEMS;

BASE CLASSIFIERS; ENSEMBLE LEARNING ALGORITHM; LOGISTIC REGRESSIONS; OVERFITTING;

TREES (MATHEMATICS);

EID: 84919832537     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. Bartlett, Peter L. and Mendelson, Shahar. Rademacher and Gaussian complexities: Risk bounds and structural results. JMLR, 3, 2002.
2. Bauer, Eric and Kohavi, Ron. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1-2): 105-139, 1999.
3. Breiman, Leo. Bagging predictors. Machine Learning, 24 (2): 123-140, 1996.
4. Breiman, Leo. Prediction games and arcing algorithms. Neural Computation, 11(7): 1493-1517, 1999.
5. Caruana, Rich, Niculescu-Mizil, Alexandru, Crew, Geoff, and Ksikes, Alex. Ensemble selection from libraries of models. In ICML, 2004.
6. Dietterich, Thomas G. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2): 139-157, 2000.
7. Duchi, John C. and Singer, Yoram. Boosting with structural sparsity. In ICML, pp. 38, 2009.
8. Freund, Yoav and Schapire, Robert E. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer System Sciences, 55(1): 119-139, 1997.
9. Freund, Yoav, Mansour, Yishay, and Schapire, Robert E. Generalization bounds for averaged classifiers. The Annals of Statistics, 32:1698-1722, 2004.
10. Friedman, Jerome, Hastie, Trevor, and Tibshirani, Robert. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 28:2000, 1998.
11. Grove, Adam J and Schuurmans, Dale. Boosting in the limit: Maximizing the margin of learned ensembles. In AAAI/IAAI, pp. 692-699, 1998.
12. Kivinen, Jyrki and Warmuth, Manfred K. Boosting as entropy projection. In COLT, pp. 134-144, 1999.
13. Koltchinskii, Vladmir and Panchenko, Dmitry. Empirical margin distributions and bounding the generalization error of combined classifiers. Annals of Statistics, 30, 2002.
14. MacKay, David J. C. Bayesian methods for adaptive models. PhD thesis, California Institute of Technology, 1991.
15. Mansour, Yishay. Pessimistic decision tree pruning based on tree size. In Proceedings of ICML, pp. 195-201, 1997.
16. Mohri, Mehryar, Rostamizadeh, Afshin, and Talwalkar, Ameet. Foundations of Machine Learning. The MIT Press, 2012.
17. Quinlan, J. Ross. Bagging, boosting, and C4.5. In AAAI/IAAI, Vol. 1, pp. 725-730, 1996.
18. Ratsch, Gunnar and Warmuth, Manfred K. Maximizing the margin with boosting. In COLT, pp. 334-350, 2002.
19. Ratsch, Gunnar and Warmuth, Manfred K. Efficient margin maximizing with boosting. Journal of Machine Learning Research, 6:2131-2152, 2005.
20. Ratsch, Gunnar, Mika, Sebastian, and Warmuth, Manfred K. On the convergence of leveraging. In NIPS, pp. 487-494, 2001a.
21. Ratsch, Gunnar, Onoda, Takashi, and Muller, Klaus- Robert. Soft margins for AdaBoost. Machine Learning, 42(3):287-320, 2001b.
22. Reyzin, Lev and Schapire, Robert E. How boosting the margin can also boost classifier complexity. In ICML, pp. 753-760, 2006.
23. Schapire, Robert E. Theoretical views of boosting and applications. In Proceedings of ALT 1999, volume 1720 of Lecture Notes in Computer Science, pp. 13-25. Springer, 1999.
24. Schapire, Robert E. The boosting approach to machine learning: An overview. In Nonlinear Estimation and Classification, pp. 149-172. Springer, 2003.
25. Schapire, Robert E., Freund, Yoav, Bartlett, Peter, and Lee, Wee Sun. Boosting the margin: A new explanation for the effectiveness of voting methods. In ICML, pp. 322- 330, 1997.
26. Smyth, Padhraic and Wolpert, David. Linearly combining density estimators via stacking. Machine Learning, 36: 59-83, July 1999.
27. Vapnik, Vladimir N. Statistical Learning Theory. Wiley- Interscience, 1998.
28. Warmuth, Manfred K., Liao, Jun, and Ratsch, Gunnar. Totally corrective boosting algorithms that maximize the margin. In ICML, pp. 1001-1008, 2006.