Book Title: Machine Learning for Spatial Environmental Data
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 | ||||||||

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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 | |||||||

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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 | ||||||||

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< < | 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 | |||||||

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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 | |||||||

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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 | ||||||||

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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 | |||||||

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< < | Support vector classification
Spatial data classification with SVM Support vector regression Advanced topics in kernel methods | |||||||

REFERENCES
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