General and Local: Averaged k-Dependence Bayesian Classifiers

General and Local: Averaged k-Dependence Bayesian Classifiers

The inference of a general Bayesian network has been shown to be an NP-hard problem, even for approximate solutions. Although k-dependence Bayesian (KDB) classifier can construct at arbitrary points (values of k) along the attribute dependence spectrum, it cannot identify the changes of interdepende...

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Bibliographic Details
Main Authors:	Limin Wang, Haoyu Zhao, Minghui Sun, Yue Ning
Format:	Article
Language:	English
Published:	MDPI AG 2015-06-01
Series:	Entropy
Subjects:	k-dependence Bayesian classifier substitution-elimination resolution functionaldependency rules of probability
Online Access:	http://www.mdpi.com/1099-4300/17/6/4134

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