Bayesian and neural networks for geographic information processing

被引：13

作者：

Stassopoulou, A ^{[1
]}

Petrou, M ^{[1
]}

Kittler, J ^{[1
]}

机构：

[1] UNIV SURREY,DEPT ELECT & ELECT ENGN,GUILDFORD GU2 5XH,SURREY,ENGLAND

来源：

PATTERN RECOGNITION LETTERS | 1996年 / 17卷 / 13期

基金：

英国工程与自然科学研究理事会;

关键词：

Bayesian networks; neural networks; conditional probability matrices; geographic information processing; desertification;

D O I：

10.1016/S0167-8655(96)00089-X

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：1325 / 1330

页数：6

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