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dc.contributor.author Jilkine, Petr en_US
dc.date.accessioned 2007-05-15T15:15:13Z
dc.date.available 2007-05-15T15:15:13Z
dc.date.issued 1997-09-01T00:00:00Z en_US
dc.identifier.uri http://hdl.handle.net/1993/735
dc.description.abstract Classification of Magnetic Resonance (MR) and Infrared (IR) spectra promises to become an effective tool for early medical diagnosis of diseases. The proposed thesis project involves the development and comparison of classification strategies and algorithms for the analysis of spectra of healthy and diseased tissue biopsies of various disease states. Several methods of aggregating outcomes of classifiers are considered in order to improve the classification accuracy, and applied to artificial and real-life spectra. Logistic regression, linear combination of classifiers, fuzzy integration, stacked generalization and some other methods of classifier aggregation, as well as different ways of estimating necessary parameters are considered. The results indicate that in many cases aggregation of classifiers improves the classification performance in comparison to that of the classifiers being aggregated. The results on real-life spectra vary. The methods perform well on some data sets and relatively poorly on others. Strategies are recommended to gain from classifier aggregation. en_US
dc.format.extent 5244808 bytes
dc.format.extent 184 bytes
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language en en_US
dc.language.iso en_US
dc.rights info:eu-repo/semantics/openAccess
dc.title Application of information fusion methods to biomedical data en_US
dc.type info:eu-repo/semantics/doctoralThesis
dc.degree.discipline Electrical and Computer Engineering en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US


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