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Bibliography

[1] Breiman, L. Bagging Predictors. Machine Learning 26, pp. 123–140, 1996.

[2] Breiman, L. Random Forests. Machine Learning 45, pp. 5–32, 2001.

[3] Breiman, L. http://www.stat.berkeley.edu/~breiman/RandomForests/

[4] Breiman, L., et al. Classification and Regression Trees. Chapman & Hall, Boca Raton, 1993.

[5] Freund, Y. A more robust boosting algorithm. arXiv:0905.2138v1, 2009.

[6] Freund, Y. and R. E. Schapire. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. J. of Computer and System Sciences, Vol. 55, pp. 119–139, 1997.

[7] Friedman, J. Greedy function approximation: A gradient boosting machine. Annals of Statistics, Vol. 29, No. 5, pp. 1189–1232, 2001.

[8] Friedman, J., T. Hastie, and R. Tibshirani. Additive logistic regression: A statistical view of boosting. Annals of Statistics, Vol. 28, No. 2, pp. 337–407, 2000.

[9] Hastie, T., R. Tibshirani, and J. Friedman. The Elements of Statistical Learning, second edition. Springer, New York, 2008.

[10] Ho, T. K. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 20, No. 8, pp. 832–844, 1998.

[11] Schapire, R. E. et al. Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, Vol. 26, No. 5, pp. 1651–1686, 1998.

[12] Zadrozny, B., J. Langford, and N. Abe. Cost-Sensitive Learning by Cost-Proportionate Example Weighting. CiteSeerX. [Online] 2003. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.5.9780

[13] Zhou, Z.-H. and X.-Y. Liu. On Multi-Class Cost-Sensitive Learning. CiteSeerX. [Online] 2006. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.92.9999

  


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