Mapping of Environmental Data Using Kernel-Based Methods
Details
Serval ID
serval:BIB_7373B543D8DB
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
Mapping of Environmental Data Using Kernel-Based Methods
Journal
Revue Internationale de Géomatique
ISSN-L
1260-5875
Publication state
Published
Issued date
2007
Peer-reviewed
Oui
Volume
17
Pages
309-331
Language
english
Abstract
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.
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.
Keywords
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)
Create date
25/11/2013 18:18
Last modification date
20/08/2019 15:31