Advanced Spatial Data Analysis and Modelling with Support Vector Machines

Details

Serval ID
serval:BIB_755761558424
Type
Article: article from journal or magazin.
Collection
Publications
Title
Advanced Spatial Data Analysis and Modelling with Support Vector Machines
Journal
International Journal of Fuzzy Systems
Author(s)
Kanevski M., Pozdnoukhov A., Canu S., Maignan M.
ISSN-L
1562-2479
Publication state
Published
Issued date
2002
Peer-reviewed
Oui
Volume
4
Pages
606-616
Language
english
Abstract
The present paper deals with novel developments and application of
Support Vector Machines (Support Vector Classifier SVC and Support
Vector Regression SVR) for the analysis and modeling of spatially
distributed environmental and pollution information (categorical
and/or continuous data). SVC/SVR models are based on the Statistical
Learning Theory or Vapnik-Chervonenkis (VC)-theory. The SVC provide
non-linear classification by mapping the input space into high dimensional
feature spaces where a special type of hyper-planes with maximal
margins (giving rise to good generalizations) are constructed. SVR
provide robust non-linear regression of spatially distributed data.
Real case studies of the present paper deal with binary classification
problem of indicator variables, multi-class classification of soil
types, and prediction mapping of radioactively contaminated territories.
Geostatistical tools (variography) is used to control the performance
of the machines and for better understanding of the results. The
SVC/SVR are well adapted to fuzzy environmental and pollution data.
Keywords
environmental spatial data classification and mapping, support vector, machines, geostatistics
Create date
25/11/2013 19:02
Last modification date
20/08/2019 15:32
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