Machine Learning for Spatial Environmental Data: Theory, Applications, and Software

Front Cover
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.
 

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Contents

LEARNING FROM GEOSPATIAL DATA
1
11 Problems and important concepts of machine learning
2
12 Machine learning algorithms for geospatial data
20
13 Contents of the book Software description
36
14 Short review of the literature
47
EXPLORATORY SPATIAL DATA ANALYSIS PRESENTATION OF DATA AND CASE STUDIES
53
22 Data preprocessing
68
Variography
70
ARTIFICIAL NEURAL NETWORKS
127
42 Radial basis function neural networks
172
43 General regression neural networks
187
44 Probabilistic neural networks
211
45 Selforganising maps
218
46 Gaussian mixture models and mixture density network
231
47 Conclusions
244
SUPPORT VECTOR MACHINES AND KERNEL METHODS
247

24 Presentation of data
75
a benchmark model for regression and classification
84
26 Conclusions to chapter 2
94
GEOSTATISTICS
95
32 Geostatistical conditional simulations
114
33 Spatial classification
122
34 Software
123
35 Conclusions
126
52 Support vector classification
253
53 Spatial data classification with SVM
267
54 Support vector regression
309
56 Advanced topics in kernel methods
327
REFERENCES
347
INDEX
373
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