Machine learning for spatial environmental data : theory, applications and software
Author(s)
Bibliographic Information
Machine learning for spatial environmental data : theory, applications and software
(Environmental science, Environmental engineering)
EPFL Press , CRC Press, c2009
- : EPFL Press
- : CRC Press
Available at 5 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
  Switzerland
  France
  Belgium
  Netherlands
  Sweden
  Norway
  United States of America
Note
Includes bibliographical reference (p. [347]-371) and index
Title of CD-ROM: Machine learning office : software for environmental spatial data analysis
Description and Table of Contents
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
by "Nielsen BookData"