Издательство EPFL Press, 2009, -380 pp.The book is devoted to the analysis, modelling and visualisation of spatial environmental data using machine learning algorithms. In a broad sense, machine learning can be considered a subfield of artificial intelligence; the subject is mainly concerned with the development of techniques and algorithms that allow computers to learn from data. In this book, machine learning algorithms are adapted for use with spatial environmental data with the goal of making spatial predictions.
Why machine learning? A brief reply would be that, as modelling tools, most machine learning algorithms are universal, adaptive, nonlinear, robust and efficient. They can find acceptable solutions for the classification, regression, and probability density modelling problems in high-dimensional geo-feature spaces, composed of geographical space and additional relevant spatially referenced features. They are well suited to be implemented as predictive engines for decision-support systems, for the purpose of environmental data mining, including pattern recognition, modelling and predictions, and automatic data mapping. They compete efficiently with geostatistical models in low-dimensional geographical spaces, but they become indispensable in high-dimensional geofeature spaces.
The book is complementary to a previous work [M. Kanevski and M. Maignan, Analysis and Modelling of Spatial Environmental Data, EPFL Press, 288 p., 2004] in which the main topics were related to data analysis using geostatistical predictions and simulations. The present book follows the same presentation: theory, applications, software tools and explicit examples. We hope that this organization will help to better understand the algorithms applied and lead to the adoption of this book for teaching and research in machine learning applications to geo- and environmental sciences. Therefore, an important part of the book is a collection of software tools – the Machine Learning Office – developed over the past ten years. The Machine Learning Office has been used both for teaching and for carrying out fundamental and applied research. We have implemented several machine learning algorithms and models of interest for geo- and environmental sciences into this software: the multilayer perceptron (a workhorse of machine learning); general regression neural networks; probabilistic neural networks; self-organizing maps; support vector machines; Gaussian mixture models; and radial basis-functions networks. Complementary tools useful for exploratory data analysis and visualisation are provided as well. The software has been optimized for user friendliness.Learning from Geospatial Data
Exploratory Spatial Data Analysis. Presentation of Data and Case Studies
Geostatistics
Artificial Neural Networks

Support Vector Machines and Kernel Methods

Издательство EPFL Press, 2009, -380 pp.该书致力于利用机器学习算法对空间环境数据进行分析、建模和可视化。从广义上讲，机器学习可以被认为是人工智能的一个子领域；该主题主要涉及到允许计算机从数据中学习的技术和算法的发展。在本书中，机器学习算法被调整为用于空间环境数据，目的是进行空间预测。

为什么是机器学习？一个简短的回答是，作为建模工具，大多数机器学习算法具有通用性、适应性、非线性、稳健性和高效性。它们可以为高维地理特征空间的分类、回归和概率密度建模问题找到可接受的解决方案，该空间由地理空间和其他相关的空间参照特征组成。它们很适合作为决策支持系统的预测引擎来实施，用于环境数据挖掘，包括模式识别、建模和预测以及自动数据映射。它们在低维地理空间中与地理统计模型有效竞争，但在高维地理特征空间中则变得不可或缺。

本书是对以前的工作的补充[M. Kanevski and M. Maignan, Analysis and Modelling of Spatial Environmental Data, EPFL Press, 288 p., 2004]，其中的主要议题与使用地理统计预测和模拟进行数据分析有关。本书遵循同样的表述方式：理论、应用、软件工具和明确的例子。我们希望这种组织方式能够帮助人们更好地理解所应用的算法，并促使人们在机器学习应用于地理和环境科学的教学和研究中采用这本书。因此，本书的一个重要部分是在过去十年中开发的软件工具集--机器学习办公室。机器学习办公室既被用于教学，也被用于开展基础和应用研究。我们在这个软件中实现了若干机器学习算法和地球和环境科学感兴趣的模型：多层感知器（机器学习的主力军）；一般回归神经网络；概率神经网络；自组织地图；支持向量机；高斯混合模型；和径向基函数网络。同时还提供了对探索性数据分析和可视化有用的补充工具。该软件已被优化为用户友好型。

探索性空间数据分析。数据和案例研究的介绍

地理统计学

人工神经网络

支持向量机和核方法