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» Hidden Markov Models with Multiple Observation Processes
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ICASSP
2010
IEEE
14 years 10 months ago
Variational nonparametric Bayesian Hidden Markov Model
The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure...
Nan Ding, Zhijian Ou
ICASSP
2011
IEEE
14 years 1 months ago
Joint modeling of observed inter-arrival times and waveform data with multiple hidden states for neural spike-sorting
We present a novel, maximum likelihood framework for automatic spike-sorting based on a joint statistical model of action potential waveform shape and inter-spike interval duratio...
Brett Matthews, Mark Clements
ICCV
2003
IEEE
15 years 12 months ago
Recognition of Group Activities using Dynamic Probabilistic Networks
Dynamic Probabilistic Networks (DPNs) are exploited for modelling the temporal relationships among a set of different object temporal events in the scene for a coherent and robust...
Shaogang Gong, Tao Xiang
WSDM
2010
ACM
322views Data Mining» more  WSDM 2010»
15 years 7 months ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant o...
Kuansan Wang, Nikolas Gloy, Xiaolong Li
TASLP
2002
84views more  TASLP 2002»
14 years 9 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