Selection of generative models in classification

@article{Bouchard2006SelectionOG,
  title={Selection of generative models in classification},
  author={Guillaume Bouchard and Gilles Celeux},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2006},
  volume={28},
  pages={544-554}
}
This paper is concerned with the selection of a generative model for supervised classification. Classical criteria for model selection assess the fit of a model rather than its ability to produce a low classification error rate. A new criterion, the Bayesian entropy criterion (BEC), is proposed. This criterion takes into account the decisional purpose of a model by minimizing the integrated classification entropy. It provides an interesting alternative to the cross-validated error rate which is… CONTINUE READING

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