2011.bib

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@inproceedings{GandarLD11,
  author = {Benoît Gandar and
	Gaëlle Loosli and
	Guillaume Deffuant},
  title = {Dispersion Effect on Generalisation Error in Classification - Experimental
	Proof and Practical Algorithm},
  booktitle = {{ICAART} 2011 - Proceedings of the 3rd International Conference on
	Agents and Artificial Intelligence, Volume 1 - Artificial Intelligence,
	Rome, Italy, January 28-30, 2011},
  pages = {703--706},
  year = {2011},
  abstract = {We consider the problem of generating learning data within the context of active learning in classification. First, we recall theoretical results proposing discrepancy as a criterion for generating sample in regression.
		We show that theoretical results about low discrepancy sequences in regression problems are not adequate for
	classification problems. Secondly we give a theoretical argument in favour of dispersion as a criterion for generating data. Then, we present numerical experiments which have a good degree of adequacy with this theory.}
}

@inproceedings{bouges2011face,
  title = {Face Detection in a Pose Different than the One Learned},
  author = {Bouges, Pierre and Chateau, Thierry and Blanc, Christophe and Loosli, Gaëlle},
  year = {2011},
  book = {GRETSI, Bordeaux, France}
}

@inproceedings{loosli2011non,
  title = {Non positive SVM},
  author = {Loosli, Gaëlle and Canu, Stéphane},
  booktitle = {OPT NIPS Workshop},
  year = {2011},
  abstract = {Learning SVM with non positive kernels is is a problem that has been addressed in the last years but it is not really solved : indeed, either the kernel is {\em corrected} (as a pre-treatment or via a modified learning scheme), either it is used with some well-chosen parameters that lead to almost positive-definite kernels. 
  In this work, we aim at solving the actual problem induced by non positive kernels, {\em i.e.} solving the stabilization system in the \krein space associated with the non-positive kernel. We first describe this stabilization system, then we expose a simple algorithm based on the eigen-decomposition of the kernel matrix. While providing satisfying solutions, the proposed algorithm shows limitations in terms of memory storage and computational effort. The direct resolution is still an open question.}
}

@unpublished{canu:cel-00643485,
  title = {{SVM and kernel machines}},
  author = {Canu, Stéphane and Loosli, Gaëlle and Rakotomamonjy, Alain},
  url = {https://cel.archives-ouvertes.fr/cel-00643485},
  note = {Lecture},
  type = {école thématique},
  hal_local_reference = {SVM},
  address = {ECI 2011, Buenos Aires},
  pages = {100},
  institution = {{Informatique}},
  year = {2011},
  pdf = {https://cel.archives-ouvertes.fr/cel-00643485/file/tutorial_04_Kernel_Advances.pdf},
  hal_id = {cel-00643485},
  hal_version = {v1}
}