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Machine Learning for Spatial Environmental Data: Theory, Applications, and Software, 1st Edition (Hardback) book cover

Machine Learning for Spatial Environmental Data

Theory, Applications, and Software, 1st Edition

By Mikhail Kanevski, Vadim Timonin, Alexi Pozdnukhov

EPFL Press

400 pages

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Hardback: 9780849382376
pub: 2009-06-09
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Description

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.

Table of Contents

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

Subject Categories

BISAC Subject Codes/Headings:
COM000000
COMPUTERS / General
MAT029000
MATHEMATICS / Probability & Statistics / General
TEC036000
TECHNOLOGY & ENGINEERING / Remote Sensing & Geographic Information Systems