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Kikuchi-Bayes: Factorized Models for Approximate Classification in Closed Form
Rezelj, Klemen (Avtor), Isaković, Tatjana (Mentor) Več o mentorju... Povezava se odpre v novem oknu, Fischinger, Matej (Mentor) Več o mentorju... Povezava se odpre v novem oknu, Jakulin, Aleks (Avtor), Rish, Irina (Avtor), Bratko, Ivan (Avtor)

URLURL - Predstavitvena datoteka, za dostop obiščite http://eprints.fri.uni-lj.si/149/ Povezava se odpre v novem oknu

Izvleček
We propose a simple family of classification models, based on the Kikuchi approximation to free energy. We note that the resulting product of potentials is not normalized, but for classification it is easy to perform the normalization for each instance separately. We propose a learning method based on including those initial regions that would otherwise be significantly different from those estimated directly. We observe that this algorithm outperforms other methods, such as the tree-augmented naive Bayes, but that the inclusion of regions may increase the approximation error, even in cases when adding a region does not yield loopy dependencies.

Jezik:Neznan jezik
Ključne besede:naive Bayesian classifier, cluster variation method, Kikuchi approximation, bootstrap
Vrsta gradiva:Delo ni kategorizirano (r6)
Organizacija:FRI - Fakulteta za računalništvo in informatiko
Leto izida:2004
Število ogledov:408
Število prenosov:119
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
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