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ICIAP
2007
ACM

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Sparseness Achievement in Hidden Markov Models

14 years 11 months ago

Sparseness Achievement in Hidden Markov Models

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In this paper, a novel learning algorithm for Hidden Markov Models (HMMs) has been devised. The key issue is the achievement of a sparse model, i.e., a model in which all irrelevant parameters are set exactly to zero. Alternatively to standard Maximum Likelihood Estimation (Baum Welch training), in the proposed approach the parameters estimation problem is cast into a Bayesian framework, with

Manuele Bicego, Marco Cristani, Vittorio Murino

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Baum Welch Training | ICIAP 2007 | Image Processing | Maximum Likelihood Estimation | Parameters Estimation Problem |

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Added	08 Dec 2009
Updated	08 Dec 2009
Type	Conference
Year	2007
Where	ICIAP
Authors	Manuele Bicego, Marco Cristani, Vittorio Murino

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