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dc.contributor.authorVan Dyk, Hendrik Oostewald
dc.descriptionThesis (M.Ing. (Computer Engineering))--North-West University, Potchefstroom Campus, 2009.
dc.description.abstractIn this dissertation we developed theoretical models to analyse Gaussian and multinomial distributions. The analysis is focused on classification in high dimensional feature spaces and provides a basis for dealing with issues such as data sparsity and feature selection (for Gaussian and multinomial distributions, two frequently used models for high dimensional applications). A Naïve Bayesian philosophy is followed to deal with issues associated with the curse of dimensionality. The core treatment on Gaussian and multinomial models consists of finding analytical expressions for classification error performances. Exact analytical expressions were found for calculating error rates of binary class systems with Gaussian features of arbitrary dimensionality and using any type of quadratic decision boundary (except for degenerate paraboloidal boundaries). Similarly, computationally inexpensive (and approximate) analytical error rate expressions were derived for classifiers with multinomial models. Additional issues with regards to the curse of dimensionality that are specific to multinomial models (feature sparsity) were dealt with and tested on a text-based language identification problem for all eleven official languages of South Africa.
dc.publisherNorth-West University
dc.subjectNaïve Bayesianen
dc.subjectMaximum likelihooden
dc.subjectCurse of dimensionalityen
dc.subjectGaussian distributionen
dc.subjectMultinomial distributionen
dc.subjectFeature selectionen
dc.subjectData sparsityen
dc.subjectChi-square variatesen
dc.subjectHyperboloidal decision boundariesen
dc.titleClassification in high dimensional feature spacesen

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  • ETD@PUK [7579]
    This collection contains the original digitized versions of research conducted at the North-West University (Potchefstroom Campus)

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