Classification with Multiple Classes using Naïve Bayes and Text Generation with a Small Data Set using a Recurrent Neural Network

dc.contributor.author	Reiten, Tore Elias Gjervik
dc.date.accessioned	2017-09-18T07:24:51Z
dc.date.available	2017-09-18T07:24:51Z
dc.date.issued	2017
dc.identifier.uri	http://hdl.handle.net/11250/2455012
dc.description	Master's thesis Information- and communication technology IKT590 - University of Agder 2017	nb_NO
dc.description.abstract	In this thesis, text classification and text generation are explored using only a small data set and many classes. This thesis experiments with text classification, and show how it is able to find the most similar output compared to the input even with thousands of classes. Furthermore, text generation is explored on a small data set to create a unique output. By using Na¨ıve Bayes text classifier combined with a Recurrent Neural Network language-model, it is possible to use new deviations as input before an original suggestion for a measure is generated as the output	nb_NO
dc.language.iso	eng	nb_NO
dc.publisher	Universitetet i Agder ; University of Agder	nb_NO
dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no	*
dc.subject	IKT590	nb_NO
dc.title	Classification with Multiple Classes using Naïve Bayes and Text Generation with a Small Data Set using a Recurrent Neural Network	nb_NO
dc.type	Master thesis	nb_NO
dc.subject.nsi	VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420	nb_NO
dc.source.pagenumber	VII, 42 p.	nb_NO

Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal