An efficient parameter estimation method for generalized Dirichlet priors in naïve Bayesian classifiers with multinomial models
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- 作者：Tzu-Tsung Wong ; tzutsung@mail.ncku.edu.tw" class="auth_mail" title="E-mail the corresponding authorAuthor Vitae ; Chao-Rui LiuAuthor Vitae
- 关键词：Covariance matrix ; Document classification ; Generalized Dirichlet distribution ; Multinomial model ; Naï ; ve Bayesian classifier
- 刊名：Pattern Recognition
- 出版年：2016
- 出版时间：December 2016
- 年：2016
- 卷：60
- 期：Complete
- 页码：62-71
- 全文大小：728 K
文摘
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An efficient method for constructing noninformative GD priors is proposed.
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The best strategy is to choose the largest candidate value for a parameter.
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A small default value is necessary for the distinct words with small frequencies.
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This method significantly improves the performance of naïve Bayesian classifiers.