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» Theory and Use of the EM Algorithm
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CSDA
2007
108views more  CSDA 2007»
13 years 8 months ago
Nonlinear random effects mixture models: Maximum likelihood estimation via the EM algorithm
Nonlinear random effects models with finite mixture structures are used to identify polymorphism in pharmacokinetic/ pharmacodynamic (PK/PD) phenotypes. An EM algorithm for maxim...
Xiaoning Wang, Alan Schumitzky, David Z. D'Argenio
ICA
2012
Springer
12 years 4 months ago
New Online EM Algorithms for General Hidden Markov Models. Application to the SLAM Problem
In this contribution, new online EM algorithms are proposed to perform inference in general hidden Markov models. These algorithms update the parameter at some deterministic times ...
Sylvain Le Corff, Gersende Fort, Eric Moulines
NN
1998
Springer
177views Neural Networks» more  NN 1998»
13 years 8 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
ICASSP
2009
IEEE
14 years 3 months ago
A variational EM algorithm for learning eigenvoice parameters in mixed signals
We derive an efficient learning algorithm for model-based source separation for use on single channel speech mixtures where the precise source characteristics are not known a pri...
Ron J. Weiss, Daniel P. W. Ellis
WCE
2007
13 years 9 months ago
Applying EM Algorithm for Segmentation of Textured Images
— Texture analysis plays an increasingly important role in computer vision. Since the textural properties of images appear to carry useful information for discrimination purposes...
K. Revathy, V. S. Roshni