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IROS
2006
IEEE
141views Robotics» more  IROS 2006»
15 years 10 months ago
Layered HMM for Motion Intention Recognition
— We evaluate Layered Hidden Markov Models (LHMM) for motion intention recognition based on actionprimitives or gestemes. The proposed methodology uses three different HMM models...
Daniel Aarno, Danica Kragic
APPROX
2008
Springer
119views Algorithms» more  APPROX 2008»
15 years 6 months ago
The Complexity of Distinguishing Markov Random Fields
Abstract. Markov random fields are often used to model high dimensional distributions in a number of applied areas. A number of recent papers have studied the problem of reconstruc...
Andrej Bogdanov, Elchanan Mossel, Salil P. Vadhan
BMCBI
2005
100views more  BMCBI 2005»
15 years 4 months ago
Evolutionary models for insertions and deletions in a probabilistic modeling framework
Background: Probabilistic models for sequence comparison (such as hidden Markov models and pair hidden Markov models for proteins and mRNAs, or their context-free grammar counterp...
Elena Rivas
NIPS
2004
15 years 5 months ago
Harmonising Chorales by Probabilistic Inference
We describe how we used a data set of chorale harmonisations composed by Johann Sebastian Bach to train Hidden Markov Models. Using a probabilistic framework allows us to create a...
Moray Allan, Christopher K. I. Williams
TSP
2010
14 years 11 months ago
Gaussian multiresolution models: exploiting sparse Markov and covariance structure
We consider the problem of learning Gaussian multiresolution (MR) models in which data are only available at the finest scale and the coarser, hidden variables serve both to captu...
Myung Jin Choi, Venkat Chandrasekaran, Alan S. Wil...