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Web classification has been attempted through many different technologies. In this paper the authors concentrate on the comparison of Neural Networks (NN), Naive Bayes (NB) and Decision Tree (DT) classifiers for the automatic analysis and classification of attribute data from training course web pages. They introduce an enhanced NB classifier and run the same data sample through the DT and NN classifiers to determine the success rate of the classifier in the training courses domain. This research shows that the enhanced NB classifier not only outperforms the traditional NB classifier, but also performs similarly as good, if not better, than some more popular, rival techniques.
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