1. Bauer E, Kohavi R. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach Learn. 1999;36(1–2):105–39.

   Article  Google Scholar 

2. Breiman L. Prediction games and arcing classifiers. Neural Comput. 1999;11(7):1493–517.

   Article  Google Scholar 

3. Freund Y, Schapire RE. A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci. 1997;55(1):119–39 (A short version appeared in the Proceedings of EuroCOLT’95).

   Article MathSciNet MATH  Google Scholar 

4. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: a statistical view of boosting with discussions. Ann Stat. 2000;28(2):337–407.

   Article MATH  Google Scholar 

5. Gao W, Zhou Z-H. On the doubt about margin explanation of boosting. Artif Intell. 2013;203:1–18.

   Article MathSciNet MATH  Google Scholar 

6. Meir R, Rätsch G. An introduction to boosting and leveraging. In: Mendelson S, Smola AJ, editors. Advanced lectures in machine learning. LNCS vol. 2600. Berlin: Springer; 2003. p. 118–83.

   Chapter  Google Scholar 

7. Opitz D, Maclin R. Popular ensemble methods: an empirical study. J Artif Intell Res. 1999;11(1):169–98.

   Article MATH  Google Scholar 

8. Reyzin L, Schapire RE. How boosting the margin can also boost classifier complexity. In: Proceedings of the 23rd International Conference on Machine Learning, Pittsburgh; 2006. p. 753–60.

   Google Scholar 

9. Schapire RE. The strength of weak learn ability. Mach Learn. 1990;5(2):197–227.

   Google Scholar 

10. Schapire RE, Freund Y, Bartlett P, Lee WS. Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat. 1998;26(5):1651–86.

    Article MathSciNet MATH  Google Scholar 

11. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; 2001. p. 511–8.

    Google Scholar 

12. Wang L, Sugiyama M, Yang C, Zhou Z.H, Feng J. On the margin explanation of boosting algorithm. In: Proceedings of the 21st Annual Conference on Learning Theory; 2008. p. 479–90.

    Google Scholar 

13. Zhou Z-H. Ensemble methods: foundations and algorithms. Boca Raton: CRC Press; 2012.

    Book  Google Scholar 

14. Zhou Z-H. Large margin distribution learning. In: Proceedings of Artificial Neural Networks in Pattern Recognition; 2014.

    Google Scholar 

Download references