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Comparison of machine learning models for hardware configuration dimensioning tool
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering.
2020 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis we answer the following questions.Is machine learning a viable replacement for Ericsson’s CPU dimensioning tool CANDI? How do di↵erent learners perform on the problem at hand? How to fairly and accurately asses the performance of di↵erent learners?A framework for training, optimizing and evaluating Scikit-learn compatible estimators and a application for predicting EPG on SSR CPU usage are created.

Place, publisher, year, edition, pages
2020. , p. 60
Series
Sebastian Havås Klug
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:ltu:diva-81868OAI: oai:DiVA.org:ltu-81868DiVA, id: diva2:1506948
External cooperation
Ericsson AB
Educational program
Computer Science and Engineering, master's level
Supervisors
Examiners
Available from: 2020-12-10 Created: 2020-12-05 Last updated: 2020-12-10Bibliographically approved

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CiteExportLink to record
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