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Approximations of Bayes Classifiers for Statistical Learning of Clusters
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, The Institute of Technology.
2006 (English)Licentiate thesis, monograph (Other academic)
Abstract [en]

It is rarely possible to use an optimal classifier. Often the classifier used for a specific problem is an approximation of the optimal classifier. Methods are presented for evaluating the performance of an approximation in the model class of Bayesian Networks. Specifically for the approximation of class conditional independence a bound for the performance is sharpened.

The class conditional independence approximation is connected to the minimum description length principle (MDL), which is connected to Jeffreys’ prior through commonly used assumptions. One algorithm for unsupervised classification is presented and compared against other unsupervised classifiers on three data sets.

Place, publisher, year, edition, pages
Matematiska institutionen , 2006. , 86 p.
Series
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1230
Keyword [en]
Pattern Recognition, Stochastic Complexity, Naïve Bayes, Bayesian Network, Classification, Clustering, Chow-Liu trees
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-5856ISBN: 91-85497-21-5 (print)OAI: oai:DiVA.org:liu-5856DiVA: diva2:21540
Presentation
2006-04-05, , Hus B, Campus Valla, Linköpings universitet, Linköping, 15:15 (English)
Opponent
Supervisors
Note
Report code: LiU-TEK-LIC 2006:11.Available from: 2006-02-22 Created: 2006-02-22

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Ekdahl, Magnus

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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