Bayesian and neural networks for geographic information processing

A. Stassopoulou, M. Petrou, J. Kittler

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


In this paper we study the problem of obtaining a correspondence between Bayesian networks and neural networks. It is shown how such a correspondence is established by obtaining a mathematical function which relates the parameters of the two models. We show the validity of our method by deriving the parameters to be used in a Bayesian network constructed to combine GIS data for assessing the risk of desertification of burned forest areas in the Mediterranean region.

Original languageEnglish
Pages (from-to)1325-1330
Number of pages6
JournalPattern Recognition Letters
Issue number13
Publication statusPublished - 25 Nov 1996


  • Bayesian networks
  • Conditional probability matrices
  • Desertification
  • Geographic information processing
  • Neural networks


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