Mapping of Environmental Data Using Kernel-Based Methods

Détails

ID Serval
serval:BIB_7373B543D8DB
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Mapping of Environmental Data Using Kernel-Based Methods
Périodique
Revue Internationale de Géomatique
Auteur(s)
Kanevski M., Pozdnoukhov A., Timonin V., Maignan M.
ISSN-L
1260-5875
Statut éditorial
Publié
Date de publication
2007
Peer-reviewed
Oui
Volume
17
Pages
309-331
Langue
anglais
Résumé
Recently, kernel-based Machine Learning methods have gained great
popularity in many data analysis and data mining fields: pattern
recognition, biocomputing, speech and vision, engineering, remote
sensing etc. The paper describes the use of kernel methods to approach
the processing of large datasets from environmental monitoring networks.
Several typical problems of the environmental sciences and their
solutions provided by kernel-based methods are considered: classification
of categorical data (soil type classification), mapping of environmental
and pollution continuous information (pollution of soil by radionuclides),
mapping with auxiliary information (climatic data from Aral Sea region).
The promising developments, such as automatic emergency hot spot
detection and monitoring network optimization are discussed as well.
Mots-clé
machine learning algorithms, kernel-based methods, Statistical Learning, Theory (SLT), General Regression Neural Networks (GRNN), Support, Vector Regression (SVR), Radial Basis Function Neural Networks (RBFNN), , Support Vector Machines (SVM), Probabilistic Neural Networks (PNN)
Création de la notice
25/11/2013 18:18
Dernière modification de la notice
03/03/2018 18:19
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