Practical Uses of Belief Functions

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

• Publication year

  1999

• Venue

  Conference on Uncertainty in Artificial Intelligence

• Publication date

  1999-07-30

• Fields of study

  Mathematics, Computer Science

• Identifiers
  arXiv 1301.6741
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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