ML Anthology

Practical Uses of Belief Functions

Philippe Smets
UAI 1999 pp. 612-621
/uai/1999/smets1999uai-practical/

Abstract

We present examples where the use of belief functions provided sound and elegant solutions to real life problems. These are essentially characterized by 'missing' information. The examples deal with 1) discriminant analysis using a learning set where classes are only partially known; 2) an information retrieval systems handling inter-documents relationships; 3) the combination of data from sensors competent on partially overlapping frames; 4) the determination of the number of sources in a multi-sensor environment by studying the intersensors contradiction. The purpose of the paper is to report on such applications where the use of belief functions provides a convenient tool to handle 'messy' data problems

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Cite

Text

Smets. "Practical Uses of Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1999.

Markdown

[Smets. "Practical Uses of Belief Functions." Conference on Uncertainty in Artificial Intelligence, 1999.](https://mlanthology.org/uai/1999/smets1999uai-practical/)

BibTeX

@inproceedings{smets1999uai-practical,
  title     = {{Practical Uses of Belief Functions}},
  author    = {Smets, Philippe},
  booktitle = {Conference on Uncertainty in Artificial Intelligence},
  year      = {1999},
  pages     = {612-621},
  url       = {https://mlanthology.org/uai/1999/smets1999uai-practical/}
}