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Authors: Alfons Juan 1 ; David Vilar 2 and Hermann Ney 2

Affiliations: 1 DSIC/ITI, Univ. Politècnica de València, Spain ; 2 Lehrstuhl für Informatik 6, RWTH Aachen, Germany

Abstract: The naive Bayes and maximum entropy approaches to text classification are typically discussed as completely unrelated techniques. In this paper, however, we show that both approaches are simply two different ways of doing parameter estimation for a common log-linear model of class posteriors. In particular, we show how to map the solution given by maximum entropy into an optimal solution for naive Bayes according to the conditional maximum likelihood criterion.

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Paper citation in several formats:
Juan, A.; Vilar, D. and Ney, H. (2007). Bridging the Gap between Naive Bayes and Maximum Entropy Text Classification. In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS; ISBN 978-972-8865-93-1, SciTePress, pages 59-65. DOI: 10.5220/0002425700590065

@conference{pris07,
author={Alfons Juan. and David Vilar. and Hermann Ney.},
title={Bridging the Gap between Naive Bayes and Maximum Entropy Text Classification},
booktitle={Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS},
year={2007},
pages={59-65},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002425700590065},
isbn={978-972-8865-93-1},
}

TY - CONF

JO - Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS
TI - Bridging the Gap between Naive Bayes and Maximum Entropy Text Classification
SN - 978-972-8865-93-1
AU - Juan, A.
AU - Vilar, D.
AU - Ney, H.
PY - 2007
SP - 59
EP - 65
DO - 10.5220/0002425700590065
PB - SciTePress