CiNii 図書 - Machine learning for spatial environmental data : theory, applications and software

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.

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

巻冊次: : EPFL Press ISBN 9782940222247

内容説明

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.

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Machine learning for spatial environmental data : theory, applications and software

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