Approximations of Bayes Classifiers for Statistical Learning of Clusters
2006 (English)Licentiate thesis, monograph (Other academic)
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.
Linköping Studies in Science and Technology. Thesis, ISSN 0280-7971 ; 1230
Pattern Recognition, Stochastic Complexity, Naïve Bayes, Bayesian Network, Classification, Clustering, Chow-Liu trees
Probability Theory and Statistics
IdentifiersURN: urn:nbn:se:liu:diva-5856ISBN: 91-85497-21-5OAI: oai:DiVA.org:liu-5856DiVA: diva2:21540
2006-04-05, , Hus B, Campus Valla, Linköpings universitet, Linköping, 15:15 (English)
Report code: LiU-TEK-LIC 2006:11.2006-02-222006-02-22