Bayesian and neural networks for geographic information processing

A. Stassopoulou; M. Petrou; J. Kittler

doi:10.1016/S0167-8655(96)00089-X

Bayesian and neural networks for geographic information processing

A. Stassopoulou, M. Petrou, J. Kittler

Department of Computer Science

University of Surrey

Research output: Contribution to journal › Article › peer-review

Abstract

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 language	English
Pages (from-to)	1325-1330
Number of pages	6
Journal	Pattern Recognition Letters
Volume	17
Issue number	13
DOIs	https://doi.org/10.1016/S0167-8655(96)00089-X
Publication status	Published - 25 Nov 1996

Keywords

Bayesian networks
Conditional probability matrices
Desertification
Geographic information processing
Neural networks

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10.1016/S0167-8655(96)00089-X

Cite this

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AU - Kittler, J.

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AB - 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.

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KW - Conditional probability matrices

KW - Desertification

KW - Geographic information processing

KW - Neural networks

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Bayesian and neural networks for geographic information processing

Abstract

Keywords

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