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Active and self-learning in a biological screening task
University of Skövde, School of Humanities and Informatics. (Information Fusion)
2009 (English)Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The focus of this master thesis is to analyze the effectiveness of self-learning and active-learning on biological virtual screening. Virtual screening is used in the pharmaceutical industry to increase the effectiveness of biological screening. Self- learning is a technique where parts of the classified test data are reused for training the classifier again. Active learning gives the classifier the possibility to select certain parts of the test data to use them as additional training data. The experiments in the thesis show that both methods can be used to improve the precision of the virtual screening process, but active-learning is more effective due to the additional information that is provided.

Place, publisher, year, edition, pages
2009. , 24 p.
National Category
Information Science
Identifiers
URN: urn:nbn:se:his:diva-3476OAI: oai:DiVA.org:his-3476DiVA: diva2:275978
Presentation
2009-06-02, E202, 13:15 (English)
Uppsok
Technology
Supervisors
Examiners
Available from: 2009-11-27 Created: 2009-11-09 Last updated: 2009-11-27Bibliographically approved

Open Access in DiVA

fulltext(259 kB)179 downloads
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Peter, Jan-Thorsten
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CiteExportLink to record
Permanent link

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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