@inproceedings{gaertner-flach-icml01,
author={Thomas Gaertner and Peter A. Flach},
title={WBC_SVM: Weighted Bayesian Classification based on Support  Vector Machines},
booktitle={Proceedings of the Eighteenth International Conference  on Machine Learning},
editor={Carla E. Brodley and Andrea Pohoreckyj Danyluk},
publisher={Morgan Kaufmann},
pages={207--209},
month={June},
year={2001},
abstract={This paper introduces an algorithm that combines nay�ve Bayes classification with feature weighting. Most of the related approaches  to feature transformation for nay�ve Bayes suggest various heuristics and non-exhaustive search strategies for selecting a subset of features with which nay�ve Bayes performs better than with the complete set of features. In contrast, the algorithm introduced in this paper employs feature weighting performed by a support vector machine. The weights are optimised such that the danger of overfitting is reduced. To the best of our knowledge, this is the  first time that nay�ve Bayes classification has been combined with feature weighting.  Experimental results on 15 UCI domains demonstrate that WBCSVM compares favourably to state-of-the-art machine learning approaches.},
abstract-url={http://www.cs.bris.ac.uk/Publications/pub_master.jsp?id=1000560},
url={http://www.cs.bris.ac.uk/Publications/Papers/1000560.pdf},
keyword={Machine Learning},
pubtype={102}
}