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Empirical Investigation of the Effect of Pruning Artificial Neural Networks With Respect to Increased Generalization Ability
Linköping University, Department of Computer and Information Science.
2010 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This final thesis covers the basics of artificial neural networks, with focus on supervised learning, pruning and the problem of achieving good generalization ability. An empirical investigation is conducted on twelve dierent problems originating from the Proben1 benchmark collection.The results indicate that pruning is more likely to improve generalization if the data is sensitive to overtting or if the networks are likely to be trapped in local minima.

Place, publisher, year, edition, pages
2010. , 53 p.
Keyword [en]
Artifical Neural Networks, Pruning, Empirical Investigation, Proben1, Magnitude Based Pruning, Optimal Brain Damage, Optimal Brain Surgeon
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:liu:diva-60112ISRN: LIU-IDA/LITH-EX-A--10/018--SEOAI: oai:DiVA.org:liu-60112DiVA: diva2:355145
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Available from: 2010-10-27 Created: 2010-10-05 Last updated: 2010-10-27Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • 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