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Model-based Learning: t-Families, Variable Selection, and Parameter Estimation

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dc.contributor.advisor McNicholas, Paul Andrews, Jeffrey Lambert 2012-08-27T19:26:18Z 2012-08-27T19:26:18Z 2012-07 2012-07-25 2012-08-27
dc.description.abstract The phrase model-based learning describes the use of mixture models in machine learning problems. This thesis focuses on a number of issues surrounding the use of mixture models in statistical learning tasks: including clustering, classification, discriminant analysis, variable selection, and parameter estimation. After motivating the importance of statistical learning via mixture models, five papers are presented. For ease of consumption, the papers are organized into three parts: mixtures of multivariate t-families, variable selection, and parameter estimation. en_US
dc.description.sponsorship Natural Sciences and Engineering Research Council of Canada through a doctoral postgraduate scholarship. en_US
dc.language.iso en en_US
dc.publisher University of Guelph en_US
dc.subject Computational Statistics en_US
dc.subject Cluster Analysis en_US
dc.subject Multivariate Statistics en_US
dc.subject Classification en_US
dc.subject Statistical Learning en_US
dc.subject Mixture Models en_US
dc.title Model-based Learning: t-Families, Variable Selection, and Parameter Estimation en_US
dc.type Thesis en_US Mathematics and Statistics en_US Doctor of Philosophy en_US Department of Mathematics and Statistics en_US
dc.rights.license All items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated. University of Guelph en_US

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