Machine learning algorithms for spatial data analysis and modelling Theory, Applications, and Software
Auteurs : Kanevski Mikhail, Timonin Vadim, Pozdnukhov Alexi
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
Date de parution : 06-2009
17x24.5 cm