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    Hierarchical mixtures of naive Bayes classifiers

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    Naive Bayes classifiers tend to perform very well on a large number of problem domains, although their representation power is quite limited compared to more sophisticated machine learning algorithms. In this pa- per we study combining multiple naive Bayes classifiers by using the hierar- chical mixtures of experts system. This system, which we call hierarchical mixtures of naive Bayes classifiers, is compared to a simple naive Bayes classifier and to using bagging and boosting for combining multiple clas- sifiers. Results on 19 data sets from the UCI repository indicate that the hierarchical mixtures architecture in general outperforms the other meth- ods
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