### Abstract

I consider a binary classification problem with a feature vector of high dimensionality. Spam mail filters are a popular example hereof. A Bayesian approach requires us to estimate the probability of a feature vector given the class of the object. Due to the size of the feature vector this is an unfeasible t ask. A useful approach is to split the feature space into several (conditionally) independent subspaces. This results in a new problem, namely how to find the " best" subdivision. In this paper I consider a weighing approach that will perform (asymptotically) as good as the best subdivision and still has a manageable complexity

Original language | English |
---|---|

Title of host publication | Proceedings of the 29th Symposium on Information Theory in the Benelux, May 29-30, 2008, Leuven, Belgium |

Editors | L. Perre, Van der, A. Dejonghe, V. Ramon |

Place of Publication | Leuven |

Publisher | IMEC |

Pages | 121-128 |

ISBN (Print) | 978-90-9023135-8 |

Publication status | Published - 2008 |

## Fingerprint Dive into the research topics of 'Four model classes for efficient Bayesian selection'. Together they form a unique fingerprint.

## Cite this

Tjalkens, T. J. (2008). Four model classes for efficient Bayesian selection. In L. Perre, Van der, A. Dejonghe, & V. Ramon (Eds.),

*Proceedings of the 29th Symposium on Information Theory in the Benelux, May 29-30, 2008, Leuven, Belgium*(pp. 121-128). IMEC.