Difference: Books20100616104047 (1 vs. 5)

Revision 52010-08-13 - TheresiaFreska

 
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.

Changed:
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Description: Table of Contents
>
>
Table of Contents
  (1) 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

(2) 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: abenchmark model for regression and classification
Conclusions to chapter

(3) GEOSTATISTICS

Spatial predictions
Geostatistical conditional simulations
Spatial classification
Software Conclusions

(4) 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

(5) 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

Deleted:
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-- TWikiAdminUser - 2010-06-04
 

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

Changed:
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<
Problems and important concepts of machine learning
>
>
Problems and important concepts of machine learning
Machine learning algorithms for geospatial data
Contents of the book. Software description
Short review of the literature
 
Deleted:
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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
Changed:
<
<
Exploratory spatial data analysis
>
>
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
 
Deleted:
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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
>
>
Spatial predictions
Geostatistical conditional simulations
Spatial classification
Software Conclusions
 
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Geostatistical conditional simulations.

Spatial classification

Software Conclusions

 (4) ARTIFICIAL NEURAL NETWORKS
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Introduction
>
>
Introduction
Radial basis function neural networks
General regression neural networks
Probabilistic neural networks
Self-organising maps
Gaussian mixture models and mixture density network
Conclusions
 
Deleted:
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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
Changed:
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<
Introduction to statistical learning theory
>
>
Introduction to statistical learning theory
Support vector classification
Spatial data classification with SVM
Support vector regression
Advanced topics in kernel methods
 
Deleted:
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<
Support vector classification

Spatial data classification with SVM

Support vector regression

Advanced topics in kernel methods

 REFERENCES

INDEX

-- TWikiAdminUser - 2010-06-04

Added:
>
>

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)

Changed:
<
<
Date of Publication:
>
>
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

Changed:
<
<
Machine learning algorithms for geospatial data
>
>
Machine learning algorithms for geospatial data
  Contents of the book. Software description
Changed:
<
<
Short review of the literature
>
>
Short review of the literature
  (2) EXPLORATORY SPATIAL DATA ANALYSIS. PRESENTATION OF DATA AND CASE STUDIES
Changed:
<
<
Exploratory spatial data analysis
>
>
Exploratory spatial data analysis
  Data pre-processing
Changed:
<
<
Spatial correlations: Variography
>
>
Spatial correlations: Variography
  Presentation of data

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

Changed:
<
<
Conclusions to chapter
>
>
Conclusions to chapter
  (3) GEOSTATISTICS

Spatial predictions

Geostatistical conditional simulations.

Changed:
<
<
Spatial classification
>
>
Spatial classification
  Software Conclusions
Changed:
<
<
(4) ARTIFICIAL NEURAL NETWORKS
>
>
(4) ARTIFICIAL NEURAL NETWORKS
 
Changed:
<
<
Introduction
>
>
Introduction
 
Changed:
<
<
Radial basis function neural networks
>
>
Radial basis function neural networks
  General regression neural networks
Changed:
<
<
Probabilistic neural networks
>
>
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

Changed:
<
<
Support vector regression
>
>
Support vector regression
  Advanced topics in kernel methods

REFERENCES

INDEX

-- TWikiAdminUser - 2010-06-04

Revision 22010-07-23 - TWikiAdminUser

Changed:
<
<
META TOPICPARENT name="GeostatisticsBooks"
>
>
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:

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

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

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

(3) GEOSTATISTICS

Spatial predictions

Geostatistical conditional simulations.

Spatial classification

Software Conclusions

(4) 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

(5) 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

-- TWikiAdminUser - 2010-06-04

Revision 12010-06-16 - TWikiAdminUser

 
META TOPICPARENT name="GeostatisticsBooks"
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:

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

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

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

(3) GEOSTATISTICS

Spatial predictions

Geostatistical conditional simulations.

Spatial classification

Software Conclusions

(4) 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

(5) 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

-- TWikiAdminUser - 2010-06-04

 
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