Conference Paper

A Fast Globally Supervised Learning Algorithm for Gaussian Mixture Models.

DOI: 10.1007/3-540-45151-X_42 Conference: Web-Age Information Management, First International Conference, WAIM 2000, Shanghai, China, June 21-23, 2000, Proceedings
Source: DBLP

ABSTRACT In this paper, a fast globally supervised learning algorithm for Gaussian Mixture Models based on the maximum relative entropy
(MRE) is proposed. To reduce the computation complexity in Gaussian component probability densities, the concept of quasi-Gaussian
probability density is used to compute the simplified probabilities. For four different learning algorithms such as the maximum
mutual information algorithm (MMI), the maximum likelihood estimation (MLE), the generalized probabilistic descent (GPD) and
the maximum relative entropy (MRE) algorithm, the random experiment approach is used to evaluate their performances. The experimental
results show that the MRE is a better alternative algorithm in accuracy and training speed compared with GPD, MMI and MLE.

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