GMMBAYES - Gaussian Mixture Model Methods. Goal of this project is to study existing and develop new methods for learning Gaussian mixture models. Furthermore, class conditional probability densities constructed by Gaussian mixture models and their usage in classification are considered during the project. The main practical goal of this project is to implement efficient classification functionality (training and classification) based on statistical theory (e.g., Bayesian inference) and Gaussian mixture model probability densities. The ML parameter estimation will be extensively studied.
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References in zbMATH (referenced in 1 article )
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- Paalanen, Pekka; Kamarainen, Joni-Kristian; Ilonen, Jarmo; Kälviäinen, Heikki: Feature representation and discrimination based on Gaussian mixture model probability densities -- practices and algorithms (2006)