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Nayve-Bayes Vs. Rule-Learning in Classification of Email

Tags:
email

Overview Growth in the use of email for communication and the corresponding growth in the volume of email received have made automatic processing of email desirable. Two learning methods, naive bayesian learning with bag-valued features and the RIPPER rule-learning algorithm have shown promise in other text categorization tasks. The author presents three experiments in automatic mail foldering and spam filtering, showing that naiÃ½ve bayes outperforms RIPPER in classification accuracy.

Further White Paper Details
Publisher	University of Texas	File Format	PDF
Date Published	March 2002	Downloads	5
Format	White Papers
Topics	Email

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