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Some Notes on Possibilistic Learning

  • J. Gebhardt
  • R. Kruse
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 363)

Abstract

We outline a possibilistic learning method for structure identification from a database of samples. In comparison to the construction of Bayesian belief networks, the proposed framework has some advantages, namely the explicit consideration of imprecise data, and the realization of a controlled form of information compression in order to increase the efficiency of the learning strategy as well as approximate reasoning using local propagation techniques.

Our learning method has been applied to reconstruct a non-singly connected network of 22 nodes and 22 arcs without the need of any a priori supplied node ordering.

Keywords

Belief Function Possibility Distribution Frame Condition Possibility Theory Approximate Reasoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Wien 1995

Authors and Affiliations

  • J. Gebhardt
    • 1
  • R. Kruse
    • 1
  1. 1.University of BraunschweigBraunschweigGermany

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