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ABSTRACT
This paper explores the use of Bayesian online classifiers to classify text documents. Empirical results indicate that these classifiers are comparable with the best text classification systems. Furthermore, the online approach offers the advantage of continuous learning in the batch-adaptive text filtering task.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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[doi> 10.1145/243199.243277]
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CITED BY 13
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Aynur Dayanik , David D. Lewis , David Madigan , Vladimir Menkov , Alexander Genkin, Constructing informative prior distributions from domain knowledge in text classification, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA
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