BoostingTree: parallel selection of weak learners in boosting, with application to ranking

Author:

Kocsis Levente,György András,Bán Andrea N.

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Reference31 articles.

1. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002a). Finite-time analysis of the multiarmed bandit problem. Machine Learning, 47(2–3), 235–256.

2. Auer, P., Cesa-Bianchi, N., Freund, Y., & Schapire, R. (2002b). The nonstochastic multiarmed bandit problem. SIAM Journal on Computing, 32, 48–77.

3. Burges, C. J. C., Svore, K. M., Bennett, P. N., Pastusiak, A., & Wu, Q. (2011). Learning to rank using an ensemble of lambda-gradient models. Journal of Machine Learning Research, 14, 25–35.

4. Busa-Fekete, R., & Kégl, B. (2009). Accelerating AdaBoost using UCB. Journal of Machine Learning Research Workshop and Conference Proceedings, 7, 111–122.

5. Busa-Fekete, R., & Kégl, B. (2010). Fast boosting using adversarial bandits. In J. Fürnkranz & T. Joachims (Eds.), Proceedings of the 27th international conference on machine learning (ICML-10) (pp. 143–150).

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