Machine Learning for Spatial Environmental Data: Theory, Applications, and Software (Environmental Sciences)

Pozdnukhov, Alexi,Timonin, Vadim,Kanevski, Mikhail

ISBN 10: 0849382378 / ISBN 13: 9780849382376

Editore: EPFL Press, 2009

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Riassunto:

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.  

Contenuti:

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 2

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

Dati bibliografici

Titolo: Machine Learning for Spatial Environmental ...
Casa editrice: EPFL Press
Data di pubblicazione: 2009
Legatura: Hardcover
Condizione: Good