New trends in Markov models and related learning to restore data

Florence Forbes (1) , Wojciech Pieczynski (2, 3, 4)

1 MISTIS - Modelling and Inference of Complex and Structured Stochastic Systems
2 TSP - CITI - Communications, Images et Traitement de l'Information
3 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
4 CNRS - Centre National de la Recherche Scientifique

We present recent approaches that extend standard Markov models and increase their modelling power. These capabilities are illustrated in the cited published works and more recently in the contributions to the Special Session on Markov models of the IEEE International Workshop on Machine Learning for Signal Processing, 2009. However, the review is not exhaustive and major older works may be missing.

Mots clés: Approximation algorithms; Bayesian method; Computational modeling; Conferences; Hidden Markov models; Image restoration; Iterative algorithms; Machine learning; Signal processing; Stochastic processes

Domaines: Intelligence artificielle [cs.AI]

Florence Forbes, Wojciech Pieczynski. New trends in Markov models and related learning to restore data. MLSP 2009 - IEEE International Workshop on Machine Learning for Signal Processing, Sep 2009, Grenoble, France. pp.1-6, ⟨10.1109/MLSP.2009.5306255⟩. ⟨hal-00781201⟩