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» Hidden Markov Models with Multiple Observation Processes
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CVIU
2004
132views more  CVIU 2004»
14 years 11 months ago
Layered representations for learning and inferring office activity from multiple sensory channels
We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at mul...
Nuria Oliver, Ashutosh Garg, Eric Horvitz
AAAI
2011
13 years 11 months ago
An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems
Recently, a number of researchers have proposed spectral algorithms for learning models of dynamical systems—for example, Hidden Markov Models (HMMs), Partially Observable Marko...
Byron Boots, Geoffrey J. Gordon
AUTOMATICA
2007
82views more  AUTOMATICA 2007»
14 years 12 months ago
Simulation-based optimal sensor scheduling with application to observer trajectory planning
The sensor scheduling problem can be formulated as a controlled hidden Markov model and this paper solves the problem when the state, observation and action spaces are continuous....
Sumeetpal S. Singh, Nikolaos Kantas, Ba-Ngu Vo, Ar...
105
Voted
CDC
2008
IEEE
157views Control Systems» more  CDC 2008»
14 years 12 months ago
A hidden Markov filtering approach to multiple change-point models
We describe a hidden Markov modeling approach to multiple change-points that has attractive computational and statistical properties. This approach yields explicit recursive filter...
Tze Leung Lai, Haipeng Xing
100
Voted
IJAR
2007
100views more  IJAR 2007»
14 years 11 months ago
Multisensor triplet Markov chains and theory of evidence
Hidden Markov chains (HMC) are widely applied in various problems occurring in different areas like Biosciences, Climatology, Communications, Ecology, Econometrics and Finances, ...
Wojciech Pieczynski