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TASLP
2002
84views more  TASLP 2002»
8 years 11 months ago
Maximum likelihood multiple subspace projections for hidden Markov models
The first stage in many pattern recognition tasks is to generate a good set of features from the observed data. Usually, only a single feature space is used. However, in some compl...
Mark J. F. Gales
ICPR
2010
IEEE
8 years 10 months ago
Audio-Visual Classification and Fusion of Spontaneous Affective Data in Likelihood Space
This paper focuses on audio-visual (using facial expression, shoulder and audio cues) classification of spontaneous affect, utilising generative models for classification (i) in t...
Mihalis A. Nicolaou, Hatice Gunes, Maja Pantic
TCSV
2008
177views more  TCSV 2008»
8 years 12 months ago
An ICA Mixture Hidden Markov Model for Video Content Analysis
In this paper, a new theoretical framework based on hidden Markov model (HMM) and independent component analysis (ICA) mixture model is presented for content analysis of video, nam...
Jian Zhou, Xiao-Ping Zhang
ICML
1999
IEEE
10 years 24 days ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
RECOMB
2003
Springer
10 years 9 days ago
Combining phylogenetic and hidden Markov models in biosequence analysis
A few models have appeared in recent years that consider not only the way substitutions occur through evolutionary history at each site of a genome, but also the way the process c...
Adam C. Siepel, David Haussler
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