Reference : Learning to rank with extremely randomized trees
Scientific congresses and symposiums : Paper published in a journal
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/84538
Learning to rank with extremely randomized trees
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Louppe, Gilles mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Jan-2011
JMLR: Workshop and Conference Proceedings
14
Yahoo! Learning to Rank Challenge
49-61
Yes
Yes
International
1938-7228
ICML Workshop - Yahoo! Learning to Rank Challenge
25 juin 2010
Haifa
Israël
[en] learning to rank ; regression trees ; ensemble methods ; transfer learning
[en] In this paper, we report on our experiments on the Yahoo! Labs Learning to Rank challenge organized in the context of the 23rd International Conference of Machine Learning (ICML 2010). We competed in both the learning to rank and the transfer learning tracks of the challenge with several tree-based ensemble methods, including Tree Bagging, Random Forests, and Extremely Randomized Trees. Our methods ranked 10th in the first track and 4th in the second track. Although not at the very top of the ranking, our results show that ensembles of randomized trees are quite competitive for the “learning to rank” problem. The paper also analyzes computing times of our algorithms and presents some post-challenge experiments with transfer learning methods.
Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
Researchers ; Professionals
http://hdl.handle.net/2268/84538
http://jmlr.csail.mit.edu/proceedings/papers/v14/

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