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Neural network ensembles

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dc.contributor.advisor Cloete I, Prof en
dc.contributor.author De Jongh A en
dc.date.accessioned 2016-09-22T08:34:32Z
dc.date.available 2016-09-22T08:34:32Z
dc.date.submitted 2004 en
dc.identifier.uri http://hdl.handle.net/20.500.11892/31050
dc.description.abstract It is possible to improve on the accuracy of a single neural network by using an ensemble of diverse and accurate networks. This thesis explores diversity in ensembles and looks at the underlying theory and mechanisms employed to generate and combine ensemble members. Bagging and boosting are studied in detail and their success in terms of well-known theoretical instruments was explained. An empirical evaluation of their performance is conducted and was compared to a single classifier and to each other in terms of accuracy and diversity. en
dc.language English en
dc.subject Interfacing and communications / Networks en
dc.title Neural network ensembles en
dc.type Masters degree en
dc.description.degree MCom en

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