Evaluation of a machine learning tool in the domain of genetic host responses

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dc.contributor.authorPitt, Joel
dc.date.accessioned2015-02-18T00:06:37Z
dc.date.available2015-02-18T00:06:37Z
dc.date.issued2003en
dc.description.abstractThis report examines the use of Support Vector Machines and Genetic Programming Classifiers (GPCs) to distinguish between classes of cancer based on gene expression data. The effect of feature selection on classifier accuracy and on the convergence time of GPCs is experimentally investigated, with the goal of making classification problems on gene expression data tractable to GPCs.en
dc.identifier.urihttp://hdl.handle.net/10092/10181
dc.language.isoen
dc.publisherUniversity of Canterbury. Computer Scienceen
dc.relation.isreferencedbyNZCUen
dc.rightsCopyright Joel Pitten
dc.rights.urihttps://canterbury.libguides.com/rights/thesesen
dc.subject.anzsrcFields of Research::46 - Information and computing sciences::4601 - Applied computing::460103 - Applications in life sciencesen
dc.subject.anzsrcField of Research::06 - Biological Sciences::0604 - Geneticsen
dc.titleEvaluation of a machine learning tool in the domain of genetic host responsesen
dc.typeReports
thesis.degree.grantorUniversity of Canterburyen
thesis.degree.levelBachelors with Honoursen
thesis.degree.nameBachelor of Science with Honoursen
uc.collegeFaculty of Engineeringen
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