Difference: Books20100616104047 (3 vs. 4)

Revision 42010-07-23 - TWikiAdminUser

 
META TOPICPARENT name="AI_GEOSTATSBooks"
Book Title: Machine Learning for Spatial Environmental Data

ISBN: 978-0-8493-8237-6

Book Author(s): Mikhail Kanevski, Alexei Pozdnoukhov and Vadim Timonin

Book Publisher: EPFL Press (distributed internationally by CRC Press)

Date of Publication: 1 June 2009

Cost: 100 US$

Pages: 380

Url: http://www.epflpress.com/livres/EPFL978-2-940222-24-7.html

Description:

The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Description: Table of Contents

(1) LEARNING FROM GEOSPATIAL DATA

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<		Problems and important concepts of machine learning
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>		Problems and important concepts of machine learning
Machine learning algorithms for geospatial data
Contents of the book. Software description
Short review of the literature
 
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<		Machine learning algorithms for geospatial data

Contents of the book. Software description

Short review of the literature

 (2) EXPLORATORY SPATIAL DATA ANALYSIS. PRESENTATION OF DATA AND CASE STUDIES
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<		Exploratory spatial data analysis
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>		Exploratory spatial data analysis
Data pre-processing
Spatial correlations: Variography
Presentation of data
k-Nearest neighbours algorithm: abenchmark model for regression and classification
Conclusions to chapter
 
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<		Data pre-processing

Spatial correlations: Variography

Presentation of data

k-Nearest neighbours algorithm: abenchmark model for regression and classification.

Conclusions to chapter

 (3) GEOSTATISTICS
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<		Spatial predictions
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>		Spatial predictions
Geostatistical conditional simulations
Spatial classification
Software Conclusions
 
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Spatial classification

Software Conclusions

 (4) ARTIFICIAL NEURAL NETWORKS
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<		Introduction
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>		Introduction
Radial basis function neural networks
General regression neural networks
Probabilistic neural networks
Self-organising maps
Gaussian mixture models and mixture density network
Conclusions
 
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<		Radial basis function neural networks

General regression neural networks

Probabilistic neural networks

Self-organising maps

Gaussian mixture models and mixture density network

Conclusions

 (5) SUPPORT VECTOR MACHINES AND KERNEL METHODS
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<		Introduction to statistical learning theory
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>		Introduction to statistical learning theory
Support vector classification
Spatial data classification with SVM
Support vector regression
Advanced topics in kernel methods
 
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<		Support vector classification

Spatial data classification with SVM

Support vector regression

Advanced topics in kernel methods

 REFERENCES

INDEX

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