Difference: Books20100616104047 (2 vs. 3)

Revision 32010-07-23 - 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)

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Date of Publication:
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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

Problems and important concepts of machine learning

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Machine learning algorithms for geospatial data
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Machine learning algorithms for geospatial data
  Contents of the book. Software description
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Short review of the literature
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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
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Spatial correlations: Variography
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Spatial correlations: Variography
  Presentation of data

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

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Conclusions to chapter
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Conclusions to chapter
  (3) GEOSTATISTICS

Spatial predictions

Geostatistical conditional simulations.

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Spatial classification
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Spatial classification
  Software Conclusions
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(4) ARTIFICIAL NEURAL NETWORKS
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(4) ARTIFICIAL NEURAL NETWORKS
 
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Introduction
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Introduction
 
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Radial basis function neural networks
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Radial basis function neural networks
  General regression neural networks
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Probabilistic neural networks
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Probabilistic neural networks
  Self-organising maps

Gaussian mixture models and mixture density network

Conclusions

(5) SUPPORT VECTOR MACHINES AND KERNEL METHODS

Introduction to statistical learning theory

Support vector classification

Spatial data classification with SVM

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Support vector regression
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Support vector regression
  Advanced topics in kernel methods

REFERENCES

INDEX

-- TWikiAdminUser - 2010-06-04

 
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