文档介绍：Spatial Bayesian Learning Algorithms for Geographic
Information Retrieval
Arron R. Walker Binh Pham Miles Moody
Queensland University of Technology Queensland University of Technology Queensland University of Technology
2 e St 2 e St 2 e St
Brisbane Q 4001 Australia Brisbane Q 4001 Australia Brisbane Q 4001 Australia
+61 7 3864 7044 +61 7 3864 9324 +61 7 3864 7044
ar.******@ b.******@ m.******@
ABSTRACT
An increasing amount of freely available Geographic Information Categories and Subject Descriptors
System (GIS) data on the has stimulated recent research [Information Search and Retrieval]: Retrieval Models.
into Geographic Information Retrieval (GIR). Typically, GIR looks
at the problem of retrieving GIS datasets on a theme by theme General Terms
basis. However in practice, themes are generally not analysed in Algorithms, Experimentation.
isolation. More often than not multiple themes are required to
create a map for a particular analysis task. To do this using the
Keywords
current GIR techniques, each theme is retrieved one by one using
Spatial Bayesian learning, learning works,
traditional retrieval methods and manually added to the map. To
geographic information system, geographic information retrieval,
automate map creation the traditional GIR paradigm of matching a
information