28605 articles – 22086 references  [version française]
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Bayesian Nonparametric Models on Decomposable Graphs
Caron F., Doucet A.
Dans Neural Information Processing Systems (2009) [inria-00419966 - version 1]
fulltext access Convergence and performances of the peeling wavelet denoising algorithm
Lacaux C., Muller A., Ranta R., Tindel S.
[hal-00433888 - version 1]
fulltext accessible on an other server Asymptotic analysis for bifurcating autoregressive processes via a martingale approach
Bercu B., de Saporta B., Gegout-Petit A.
Electronic Journal of Probability 14, 87 (2009) 2492-2526 [hal-00293341 - version 1]
fulltext access Sensitivity analysis of GreenLab model for maize
Wu Q., Cournède P.-H.
Dans 3rd international symposium on Plant Growth and Applications (PMA09) (2010) [inria-00532950 - version 1]
fulltext access Many-to-Many Graph Matching: a Continuous Relaxation Approach
Zaslavskiy M., Bach F., Vert J.-P.
(01/11/2009) [hal-00465916 - version 1]
fulltext access Robust supervised classification with mixture models: Learning from data with uncertain labels
Bouveyron C., Girard S.
Pattern Recognition 42, 11 (2009) 2649-2658 [hal-00325263 - version 2]
fulltext access Self-concordant analysis for logistic regression
Bach F.
(23/10/2009) [hal-00426227 - version 1]
fulltext access CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series
Varoquaux G., Sadaghiani S., Poline J. B., Thirion B.
Dans Medical Image Computing and Computer Aided Intervention (2009) 1 [hal-00435262 - version 1]
fulltext access EEG segmentation through time­-varying PCA
Rio M., Hutt A., Girau B.
In Neurocomp 2009 (2009) p. P-28 [hal-00402047 - version 1]
fulltext access Structured Sparse Principal Component Analysis
Jenatton R., Obozinski G., Bach F.
We present an extension of sparse PCA, or sparse dictionary learning, where the sparsity patterns of all dictionary elements are structured and constrained to belong to a prespecified set of shapes. This \emph(structured sparse PCA) is based on a structured regularization recently introduced by (1). While classical sparse priors only deal with \textit(cardinality), the regularization we use encodes higher-order information about the data. We propose an efficient and simple optimization procedur (2009) [hal-00414158 - version 3]