Using Convolutional Autoencoders to Improve Classification Performance

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dc.contributor.advisorGerven, M.A.J. van
dc.contributor.advisorGüçlü, U.
dc.contributor.authorRiemens, J.M.
dc.date.issued2015-07-13
dc.description.abstractThis thesis combines convolutional neural networks with autoencoders, to form a convolutional autoencoder. Several techniques related to the realisation of a convolutional autoencoder are investigated, and an attempt is made to use these models to improve performance on an audio-based phone classification task.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/261
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationBachelor Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeBacheloren_US
dc.titleUsing Convolutional Autoencoders to Improve Classification Performanceen_US
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