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

----- 机器学习算法：空间数据与模型

ISBN: 9780849382376 出版年：2009 页码：383 Kanevski, Mikhail Timonin, Vadim Pozdnukhov, Alexi EPFL Press

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