Department of Computer Science and Engineering UNIVERSITY OF GOTHENBURG CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2018 Statistical Model Update Optimization in Industrial Practice

dc.contributor.author	Cindroi, Maria-Bianca
dc.contributor.author	Iheanacho Mgbah, Robinson
dc.date.accessioned	2019-11-19T14:35:34Z
dc.date.available	2019-11-19T14:35:34Z
dc.date.issued	2019-11-19
dc.identifier.uri	http://hdl.handle.net/2077/62559
dc.description.abstract	This thesis presents a study done on optimizing machine learning model updates. The department of Quality and Functionality in a multinational telecommunication company is searching for an optimal solution to the problem of when, and how, to trigger a training cycle of a statistical model on their test execution dataset. We have investigated techniques regarding the possibilities of optimizing a statistical model update. A case-study has been conducted, using a telecommunication company as a case subject company.	sv
dc.language.iso	eng	sv
dc.subject	machine learning model	sv
dc.subject	optimization	sv
dc.subject	changing models	sv
dc.title	Department of Computer Science and Engineering UNIVERSITY OF GOTHENBURG CHALMERS UNIVERSITY OF TECHNOLOGY Gothenburg, Sweden 2018 Statistical Model Update Optimization in Industrial Practice	sv
dc.type	text
dc.setspec.uppsok	Technology
dc.type.uppsok	M2
dc.contributor.department	Göteborgs universitet/Institutionen för data- och informationsteknik	swe
dc.contributor.department	University of Gothenburg/Department of Computer Science and Engineering	eng
dc.type.degree	Student essay