New trends in Markov models and related learning to restore data

Abstract

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.

Using Markov Models to Mine Temporal and Spatial Data

“…Initially developed for pattern matching (Baker, 1974;Geman & Geman, 1984) and information theory (Forney, 1973), they have shown good modelling capabilities in various problems occurring in different areas like Biosciences (Churchill, 1989), Ecology (Li et al, 2001;Le Ber et al, 2006), Image (Pieczynski, 2003;Forbes & Pieczynski, 2009) and Signal processing (Rabiner & Juang, 1995). These stochastic models assume that the signals under investigation have a local property -called the Markov property-which states that the signal evolution at a given instant or around a given location is uniquely determined by its neighbouring values.…”

New Fundamental Technologies in Data Mining

“…P − decrypts and sends it to (Baker, 1974;Geman & Geman, 1984) and information theory (Forney, 1973), they have shown good modelling capabilities in various problems occurring in different areas like Biosciences (Churchill, 1989), Ecology (Li et al, 2001;Le Ber et al, 2006), Image (Pieczynski, 2003;Forbes & Pieczynski, 2009) and Signal processing (Rabiner & Juang, 1995). These stochastic models assume that the signals under investigation have a local property -called the Markov property-which states that the signal evolution at a given instant or around a given location is uniquely determined by its neighbouring values.…”