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    Analysis of the Use of Background Distribution for Naive Bayes Classifiers
    with Akihiro Tamura and Masaaki Tsuchida
    Journal of Intelligent Systems 28 (2): 259-273. 2019.
    The naive Bayes classifier is a popular classifier, as it is easy to train, requires no cross-validation for parameter tuning, and can be easily extended due to its generative model. Moreover, recently it was shown that the word probabilities estimated from large unlabeled corpora could be used to improve the parameter estimation of naive Bayes. However, previous methods do not explicitly allow to control how much the background distribution can influence the estimation of naive Bayes parameters…Read more