## Abstract

When a Bayesian classifier is designed, a model for the class probability density functions (PDFs) has to be chosen. This choice is determined by a trade-off between robustness and low complexity -- which is usually satisfied by simple parametric models, based on a restricted number of parameters -- and the model's ability to fit a large class of PDFs -- which usually requires a high number of model parameters. In this paper, a model is introduced, where the class PDFs are approximated as piecewise multi-linear functions (a generalisation of bilinear functions for an arbitrary dimensionality). This model is compared with classical parametric and non-parametric models, from a point of view of versatility, robustness and complexity. The results of classification and PDF estimation experiments are discussed.

Original language | English |
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Title of host publication | SSPR 2000, SPR 2000, Proc. Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recog- nition and Statistical Pattern Recognition; Alicante, Spain; August 30 - September 1, 2000. |

Publisher | The Joint IAPR Intl. Workshops on Syntactical and Structural Pattern Recognition (SSPR 2000) and Statistical Pattern Recognition (SPR 2000), pp. 501-510, Alicante, Spain. |

Pages | 501-510 |

Number of pages | 10 |

Publication status | Published - 30 Aug 2000 |

Event | Unknown - Duration: 1 Jan 2000 → … |

### Conference

Conference | Unknown |
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Period | 1/01/00 → … |