Aleks Jakulin and Irina Rish and Ivan Bratko (2004) Kikuchi-Bayes: Factorized Models for Approximate Classification in Closed Form.
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.
|Item Type: ||Article|
|Keywords: ||naive Bayesian classifier, cluster variation method, Kikuchi approximation, bootstrap|
|Language of Content: ||English|
|Related URLs: |
|Institution: ||University of Ljubljana|
|Department: ||Faculty of Computer and Information Science|
|Divisions: ||Faculty of Computer and Information Science > Artificial Intelligence Laboratory|
|Item ID: ||149|
|Date Deposited: ||27 Oct 2004|
|Last Modified: ||13 Aug 2011 00:32|
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- Kikuchi-Bayes: Factorized Models for Approximate Classification in Closed Form (deposited 27 Oct 2004)[Currently Displayed]
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