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Text filtering by boosting naive Bayes classifiers
2000
Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '00
Several machine learning algorithms have recently been used for text categorization and filtering. In particular, boosting methods such as AdaBoost have shown good performance applied to real text data. However, most of existing boosting algorithms are based on classifiers that use binary-valued features. Thus, they do not fully make use of the weight information provided by standard term weighting methods. In this paper, we present a boosting-based learning method for text filtering that uses
doi:10.1145/345508.345572
dblp:conf/sigir/KimHZ00
fatcat:7djpbpqrbza5pacggbgvytzzqi