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.This is a new textbook from Ch. Bishop. It reflects recent ... No previous knowledge of pattern recognition or machine learning concepts is assumed. ... This text serves as a cookbook for neural network solutions to practical problems using C++.
|Title||:||Machine Learning for Spatial Environmental Data|
|Author||:||Mikhail Kanevski, Vadim Timonin, Alexi Pozdnukhov|
|Publisher||:||CRC Press - 2009-06-09|