Reference : Kernelizing the output of tree-based methods
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/25765
Kernelizing the output of tree-based methods
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
d Alché-Buc, Florence [Université d'Evry > IBISC FRE CNRS 2871 > > >]
2006
Proceedings of the 23rd International Conference on Machine Learning
Acm
345--352
Yes
No
International
23rd International Conference on Machine Learning
June 25-29, 2006
Pittsburgh
USA
[en] bioinformatics ; machine learning
[en] We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the output space. The resulting algorithm, called
output kernel trees (OK3), generalizes classification and regression trees as well as tree-based ensemble methods in a principled way. It inherits several features of these methods such as interpretability, robustness to irrelevant variables, and input scalability. When only the Gram matrix over the outputs of the learning sample is given, it learns the output kernel as a function of inputs. We show that the proposed algorithm works well on an image reconstruction task and on a biological network inference problem.
http://hdl.handle.net/2268/25765
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2006/GWD06

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