
eBook - PDF
Machine Learning for Spatial Environmental Data
Theory, Applications and Software
- English
- PDF
- Available on iOS & Android
eBook - PDF
Machine Learning for Spatial Environmental Data
Theory, Applications and Software
About this book
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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Yes, you can access Machine Learning for Spatial Environmental Data by Mikhail Kanevski, Alexei Pozdnoukhov, Vadim Timonin in PDF and/or ePUB format. We have over one million books available in our catalogue for you to explore.
Information
Print ISBN
9782940222247eBook ISBN
9782889149582Table of contents
- PREFACE
- TABLE OF CONTENTS
- 1 LEARNING FROM GEOSPATIAL DATA
- 2 EXPLORATORY SPATIAL DATA ANALYSIS. PRESENTATION OF DATA AND CASE STUDIES
- 3 GEOSTATISTICS
- 4 ARTIFICIAL NEURAL NETWORKS
- 5 SUPPORT VECTOR MACHINES AND KERNEL METHODS
- REFERENCES
- INDEX