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  title = {Les suites à discrépance faible: un moyen de réduire le nombre de vecteurs supports des svms},
  author = {Gandar, Benoît and Deffuant, Guilaume and Loosli, Gaëlle},
  journal = {12ieme Journée Scientifique de l’Ecole Doctorale SPI: Apprentissage statistique-Apprentissage symbolique. Annales scientifiques de l’Université Blaise Pascal, Clermont-Ferrand II},
  year = {2008}
  title = {BALK: Bandwidth Autosetting for SVM with Local Kernels Application to data on incomplete grids},
  author = {Loosli, Gaëlle  and Deffuant, Guillaume and Canu, Stéphane},
  booktitle = {Conférence francophone sur l'apprentissage automatique, Ile de Porquerolles, France},
  year = {2008},
  abstract = {This paper focuses on the use of Support Vector Machines (SVM) when learning data located on incomplete grids. 
  We identify here two typical behaviours to be avoided, that we call holes. Holes are regions of the space with no training data where the decision changes. 
  We propose a novel algorithm which aims at preventing holes to appear. It automatically selects the local kernel bandwidth during training. 
  We provide hard-margin and soft-margin versions and several experimental results. Even though our method is designed for a specific application, it turns out that it can be applied to more general problems.}