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  • 标题:Naïve Bayes Classifier with Various Smoothing Techniques for Text Documents
  • 本地全文:下载
  • 作者:Shruti Aggarwal ; Devinder Kaur
  • 期刊名称:International Journal of Computer Trends and Technology
  • 电子版ISSN:2231-2803
  • 出版年度:2013
  • 卷号:4
  • 期号:4-5
  • 出版社:Seventh Sense Research Group
  • 摘要:Due to huge amount of increase in text data, its classification has become an important issue, now days. There are many good classification techniques discussed in this paper. Each classification method has its own assumptions, advantages and limitations. One of the most widely used classifier is Naïve Bayes which performs well with different data sets. Various Smoothing techniques are applied on Naïve Bayes. The idea behind them is to improve the classification accuracy and performance.
  • 关键词:Text classification; Naïve Bayes; Jelinek-Mercer; Smoothing; Dirichlet; Two-Stage; Absolute Discounting
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