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dc.contributor.author Nikouline, Alexandre en_US
dc.date.accessioned 2007-05-17T12:39:15Z
dc.date.available 2007-05-17T12:39:15Z
dc.date.issued 1998-03-01T00:00:00Z en_US
dc.identifier.uri http://hdl.handle.net/1993/1521
dc.description.abstract We introduce a global feature extraction method specifically designed to preprocess magnetic resonance spectra of biomedical origin. Such preprocessing is essential for the accurate and reliable classification of diseases or disease stages manifest in the spectra. The new method is Genetic Algorithm-guided. It is compared with our enhanced version of the Forward Selection algorithm ("Dynamic Programming"). Both seek and select optimal spectral subregions. These subregions necessarily retain spectral information, thus aiding the eventual identification of the biochemistry of disease presence and progression. Both methods proved to be very useful for large datasets. The danger of overfitting related to the small number of samples in the datasets was demonstrated for both the artificial and real-life data. A bilinear regression model was used to quantitate the consequences of overfitting. Taking this in account, optimal parameters for the GA guided algorithm were recommended. en_US
dc.format.extent 4246494 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.title New preprocessing methods for better classification of MR and IR spectra en_US
dc.degree.discipline Electrical and Computer Engineering en_US
dc.degree.level Doctor of Philosophy (Ph.D.) en_US


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