lazyBayesianRules · Lazy Bayesian Rules Classifier. The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. Lazy Bayesian Rules selectively relaxes the independence assumption, achieving lower error rates over a range of learning tasks. LBR defers processing to classification time, making it a highly efficient and accurate classification algorithm when small numbers of objects are to be classified. For more information, see: Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.

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• Latest Stable: 1.0.2.jar

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Version		Vulnerabilities	Size	Updated
1.0.x	lazyBayesianRules-1.0.2.jar		13.14 KB	Apr 26, 2012
	lazyBayesianRules-1.0.1.jar		13.13 KB	Apr 24, 2012

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weka.classifiers.lazy

├─ weka.classifiers.lazy.LBR.class - [JAR]