Machine learning for spatial environmental data : theory, applications and software
著者
書誌事項
Machine learning for spatial environmental data : theory, applications and software
(Environmental science, Environmental engineering)
EPFL Press , CRC Press, c2009
- : EPFL Press
- : CRC Press
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注記
Includes bibliographical reference (p. [347]-371) and index
Title of CD-ROM: Machine learning office : software for environmental spatial data analysis
内容説明・目次
内容説明
This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine learning and their application to spatial data. The text also includes real case studies based on environmental and pollution data. It includes a CD-ROM with software that will allow both students and researchers to put the concepts to practice.
目次
PREFACE
LEARNING FROM GEOSPATIAL DATA
Problems and important concepts of machine learning
Machine learning algorithms for geospatial data
Contents of the book Software description
Short review of the literature
EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES Exploratory spatial data analysis
Data pre-processing
Spatial correlations: Variography
Presentation of data
k-Nearest neighbours algorithm: a benchmark model for regression and classification
Conclusions to chapter
GEOSTATISTICS
Spatial predictions
Geostatistical conditional simulations
Spatial classification
Software
Conclusions
ARTIFICIAL NEURAL NETWORKS
Introduction
Radial basis function neural networks
General regression neural networks
Probabilistic neural networks
Self-organising maps
Gaussian mixture models and mixture density network
Conclusions
SUPPORT VECTOR MACHINES AND KERNEL METHODS
Introduction to statistical learning theory
Support vector classification
Spatial data classification with SVM
Support vector regression
Advanced topics in kernel methods
REFERENCES
INDEX
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