Reference : Segment and combine: a generic approach for supervised learning of invariant classifi...
Scientific congresses and symposiums : Paper published in a book
Engineering, computing & technology : Computer science
http://hdl.handle.net/2268/12598
Segment and combine: a generic approach for supervised learning of invariant classifiers from topologically structured data
English
Geurts, Pierre mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
Marée, Raphaël mailto [Université de Liège - ULg > > GIGA-Management : Plateforme bioinformatique >]
Wehenkel, Louis mailto [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]
2006
Proceedings of the Machine Learning Conference of Belgium and The Netherlands (Benelearn)
15-23
Yes
International
Annual Machine Learning Conference of Belgium and The Netherlands
du 11 au 12 mai 2006
Gent
Belgique
[en] bioinformatics ; machine learning ; biomagnet
[en] A generic method for supervised classification of structured objects is presented. The
approach induces a classifier by (i) deriving a surrogate dataset from a pre-classified
dataset of structured objects, by segmenting them into pieces, (ii) learning a model relating pieces to object-classes, (iii) classifying structured objects by combining predictions made for their pieces. The segmentation allows to exploit local information and can be adapted to inject invariances into the resulting classifier. The framework is illustrated on practical sequence, time-series and image classification problems.
Politique Scientifique Fédérale (Belgique) = Belgian Federal Science Policy ; Fonds de la Recherche Scientifique (Communauté française de Belgique) - F.R.S.-FNRS
http://hdl.handle.net/2268/12598
http://www.montefiore.ulg.ac.be/services/stochastic/pubs/2006/GMW06

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