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
收藏
页码：1325 / 1330
页数：6
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