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ICASSP
2011
IEEE
12 years 9 months ago
On selecting the hyperparameters of the DPM models for the density estimation of observation errors
The Dirichlet Process Mixture (DPM) models represent an attractive approach to modeling latent distributions parametrically. In DPM models the Dirichlet process (DP) is applied es...
Asma Rabaoui, Nicolas Viandier, Juliette Marais, E...
ICCV
2007
IEEE
14 years 7 months ago
Variational Segmentation using Fuzzy Region Competition and Local Non-Parametric Probability Density Functions
We describe a novel variational segmentation algorithm designed to split an image in two regions based on their intensity distributions. A functional is proposed to integrate the ...
Benoit Mory, Roberto Ardon, Jean-Philippe Thiran
CDC
2008
IEEE
130views Control Systems» more  CDC 2008»
13 years 12 months ago
Predictor estimation via Gaussian regression
Abstract— A novel nonparametric paradigm to model identification has been recently proposed where, in place of postulating finite-dimensional models of the system transfer func...
Gianluigi Pillonetto, Alessandro Chiuso, Giuseppe ...
BMCBI
2006
160views more  BMCBI 2006»
13 years 5 months ago
MIMAS: an innovative tool for network-based high density oligonucleotide microarray data management and annotation
Background: The high-density oligonucleotide microarray (GeneChip) is an important tool for molecular biological research aiming at large-scale detection of small nucleotide polym...
Leandro Hermida, Olivier Schaad, Philippe Demougin...
CVPR
2009
IEEE
15 years 18 days ago
Intrinsic Mean Shift for Clustering on Stiefel and Grassmann Manifolds
The mean shift algorithm, which is a nonparametric density estimator for detecting the modes of a distribution on a Euclidean space, was recently extended to operate on analytic ...
Hasan Ertan Çetingül, René Vida...