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Classification of chirp signals using hierarchical bayesian learning and MCMC methods

Davy, Manuel and Doncarli, Christian and Tourneret, Jean-Yves Classification of chirp signals using hierarchical bayesian learning and MCMC methods. (2002) IEEE Transactions on Signal Processing, vol. 5 (n° 2). pp. 377-388. ISSN 1053-587X

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Official URL: http://dx.doi.org/10.1109/78.978392

Abstract

This paper addresses the problem of classifying chirp signals using hierarchical Bayesian learning together with Markov chain Monte Carlo (MCMC) methods. Bayesian learning consists of estimating the distribution of the observed data conditional on each class from a set of training samples. Unfortunately, this estimation requires to evaluate intractable multidimensional integrals. This paper studies an original implementation of hierarchical Bayesian learning that estimates the class conditional probability densities using MCMC methods. The performance of this implementation is first studied via an academic example for which the class conditional densities are known. The problem of classifying chirp signals is then addressed by using a similar hierarchical Bayesian learning implementation based on a Metropolis-within-Gibbs algorithm.

Item Type:Article
Additional Information:This publication is available at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=78
Audience (journal):International peer-reviewed journal
Uncontrolled Keywords:
Institution: Université de Toulouse > Institut National Polytechnique de Toulouse - INPT
Other partners > Université Lille 1, Sciences et Technologies - Lille 1 (FRANCE)
Université de Toulouse > Université Paul Sabatier-Toulouse III - UPS
Other partners > Université de Nantes (FRANCE)
French research institutions > Centre National de la Recherche Scientifique - CNRS
Other partners > Ecole Centrale de Lille (FRANCE)
Laboratory name:
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Deposited By: Jean-yves TOURNERET
Deposited On:16 Sep 2009 14:12

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