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Rapport (Rapport De Recherche) Année : 1995

EM Algorithms for Probabilistic Mapping Networks

Résumé

The Expectation-Maximization (EM) algorithm is a general technique for maximum likelihood estimation (MLE). In this paper we present several of the important theoretical and practical issues associated with Gaussian mixture modeling (GMM) within the EM framework. First, we propose an EM algorithm for estimating the parameters of a special GMM structure, named a probablistic mapping network (PMN), where the Gaussian probability density function is realized as an internal node. In this way, the EM algorithm is extended to deal with the supervised learning of a multicategory classification problem and serve as a parameter estimator of the neural network Gaussian classifier. Then, a generalized EM (GEM) algorithm is developed as an alternative to the MLE problem of PMN. This is followed by a discussion on the computational considerations and algorithmic comparisons. It is shown that GEM converges faster than EM to the same solution space. The computational efficiency and the numerical stability of the training algorithm benefit from the well-established EM framework. The effectiveness of the proposed PMN architecture and developed EM algorithms are assessed by conducting a set of speaker recognition experiments
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Dates et versions

inria-00074071 , version 1 (24-05-2006)

Identifiants

  • HAL Id : inria-00074071 , version 1

Citer

Haizhou Li, Yifan Gong, Jean-Paul Haton. EM Algorithms for Probabilistic Mapping Networks. [Research Report] RR-2614, INRIA. 1995, pp.39. ⟨inria-00074071⟩
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