lnu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
On Bayesian optimization and its application to hyperparameter tuning
Linnaeus University, Faculty of Technology, Department of Mathematics.
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

This thesis introduces the concept of Bayesian optimization, primarly used in optimizing costly black-box functions. Besides theoretical treatment of the topic, the focus of the thesis is on two numerical experiments. Firstly, different types of acquisition functions, which are the key components responsible for the performance, are tested and compared. Special emphasis is on the analysis of a so-called exploration-exploitation trade-off. Secondly, one of the most recent applications of Bayesian optimization concerns hyperparameter tuning in machine learning algorithms, where the objective function is expensive to evaluate and not given analytically. However, some results indicate that much simpler methods can give similar results. Our contribution is therefore a statistical comparison of simple random search and Bayesian optimization in the context of finding the optimal set of hyperparameters in support vector regression. It has been found that there is no significant difference in performance of these two methods.

Place, publisher, year, edition, pages
2018. , p. 54
Keywords [en]
Optimization, Bayesian statistics, Hyperparameter tuning, Machine learning
National Category
Mathematics
Identifiers
URN: urn:nbn:se:lnu:diva-74962OAI: oai:DiVA.org:lnu-74962DiVA, id: diva2:1213125
Subject / course
Matematik/tillämpad matematik
Educational program
Applied Mahtematics Programme, 180 credits
Supervisors
Examiners
Available from: 2018-06-04 Created: 2018-06-04 Last updated: 2018-06-04Bibliographically approved

Open Access in DiVA

fulltext(2126 kB)24 downloads
File information
File name FULLTEXT01.pdfFile size 2126 kBChecksum SHA-512
d83021849ada86a1d7c32219a66fc6826c311a13c8046584682baaa19ccc6112ab4b19dd6ba799b29eaf1713695946aef77da7082363aaf089893ee6b044e353
Type fulltextMimetype application/pdf

Search in DiVA

By author/editor
Matosevic, Antonio
By organisation
Department of Mathematics
Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 24 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 65 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf