Machine learning for spatial environmental data. Theory, applications and software

Détails

ID Serval
serval:BIB_D78176059F9E
Type
Livre: un livre et son éditeur.
Collection
Publications
Institution
Titre
Machine learning for spatial environmental data. Theory, applications and software
Auteur⸱e⸱s
Mikhail Kanevski, Alexei Pozdnoukhov, Vadim Timonin
Editeur
EPFL Press
ISBN
ISBN: 978-2-940222-24-7
Statut éditorial
Publié
Date de publication
05/2009
Editeur⸱rice scientifique
EPFL Press
Langue
anglais
Nombre de pages
368
Notes
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 - Geostatistics: Spatial Predictions - Geostatistical Conditional Simulations - Spatial Classification - Software - Machine Learning Algorithms: 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 ? Support Vector Machines And Kernel Methods: Introduction to Statistical Learning Theory - Support Vector Classification - Spatial Data Classification with SVM - Support Vector Regression - Spatial Data Mapping with SVR - Advanced Topics in Kernel Methods.
Résumé
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.
Mots-clé
machine learning, environmental data mining, support vector machines, software
Création de la notice
05/04/2012 23:03
Dernière modification de la notice
20/08/2019 15:57
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