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CIMCA
2005
IEEE

Statistical Learning Procedure in Loopy Belief Propagation for Probabilistic Image Processing

11 years 11 months ago
Statistical Learning Procedure in Loopy Belief Propagation for Probabilistic Image Processing
We give a fast and practical algorithm for statistical learning hyperparameters from observable data in probabilistic image processing, which is based on Gaussian graphical model and maximum likelihood estimation. Although hyperparameters in the probabilistic model are determined so as to maximize a marginal likelihood, a practical algorithm is described for the EM algorithm with the loopy belief propagation which is one of approximate inference algorithms in artificial intelligence.
Kazuyuki Tanaka
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CIMCA
Authors Kazuyuki Tanaka
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