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Searching for authors named "Christophe Ambroise" – sorted by Relevance.

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Help! 8 documents found, showing 1 through 8.
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  • Spatial Clustering and the EM Algorithm  
  • by Christophe Ambroise, Gérard Govaert
  • …A clustering algorithm for spatial data is presented. It seeks a fuzzy partition which is optimal according to some criterion. We propose to penalize the energy function exhibited by Hathway (1986) with a term taking into account spatial contiguity constraints. The structure of the EM algorithm may …
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  • Learning From an Imprecise Teacher: Probabilistic and Evidential Approaches  
  • by Christophe Ambroise, Thierry Denoeux, Gérard Govaert, Philippe Smets
  • …. A type of learning problem is considered, in which the class of training examples is only partially specied. Two approaches to such problems are described: the maximum likelihood approach, in which a probabilistic model relating the imprecise label to the true class is postulated, and the Transfer…
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  • Hierarchical Clustering of Self-Organizing Maps for Cloud Classification  
  • by Christophe Ambroise, Fouad Badran, Sylvie Thiria — 2000 — Neurocomputing
  • …This paper presents a new method for segmenting multispectral satellite images. The proposed method is unsupervised and consists of two steps. During the rst step the pixels of a learning set are summarized by a set of codebook vectors using a Probabilistic Self-Organizing Map (PSOM, [9]) In a secon…
  • Cited by 5 (0 self) – Add To MetaCart
  • Boosting Mixture Models for Semi-supervised Learning  
  • by Yves Grandvalet, Florence d'Alché-Buc, Christophe Ambroise — in ICANN 2001,Vienne
  • …This paper introduces MixtBoost, a variant of AdaBoost dedicated to solve problems in which both labeled and unlabeled data are available. We propose several de nitions of loss for unlabeled data, from which margins are de ned. The resulting boosting schemes implement mixture models as base cl…
  • Cited by 8 (1 self) – Add To MetaCart
  • Regularization Methods for Additive Models  
  • by Marta Avalos Yves, Yves Gr, Christophe Ambroise — 2003 — Lect. Notes Comput. Sci
  • …This paper tackles the problem of model complexity in the context of additive models. Several methods have been proposed to estimate smoothing parameters, as well as to perform variable selection.…
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  • Discriminative Classification vs Modeling Methods in CBIR  
  • by Philippe H. Gosselin, Micheline Najjar, Matthieu Cord, Christophe Ambroise — 2004 — In IEEE Advanced Concepts for Intelligent Vision Systems (ACIVS
  • …Statistical learning methods are currently considered with an increasing interest in the content-based image retrieval (CBIR) community. We compare in this article two leader techniques for classification tasks. The first method uses one-class and two-class SVM to discriminate data. The second appro…
  • Cited by 2 (2 self) – Add To MetaCart
  • Semi-Supervised MarginBoost  
  • by F. d'Alché-Buc, Universite P. Et M. Curie, Yves Grandvalet, Christophe Ambroise — 2002
  • …In many discrimination problems a large amount of data is available but only a few of them are labeled. This provides a strong motivation to improve or develop methods for semi-supervised learning. In this paper, boosting is generalized to this task within the optimization framework of MarginBoost. …
  • Cited by 13 (0 self) – Add To MetaCart
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