A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines.

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The treatment is comprehensive and self-contained, targeted at researchers and students in machine learning and applied statistics. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes.

对高斯过程的全面和独立的介绍，它为内核机器的学习提供了一种原则性的、实用的、概率性的方法。

高斯过程（GPs）为内核机器的学习提供了一种原则性的、实用的、概率性的方法。在过去的十年中，GPs在机器学习界受到了越来越多的关注，本书对机器学习中的GPs的理论和实践方面提供了一个长期需要的系统和统一的处理。本书内容全面，自成体系，主要针对机器学习和应用统计的研究人员和学生。本书讨论了回归和分类的监督学习问题，并包括详细的算法。书中介绍了各种各样的协方差（核）函数，并讨论了它们的特性。从贝叶斯和经典的角度讨论了模型选择。讨论了与机器学习和统计学中其他著名技术的许多联系，包括支持向量机、神经网络、样条、正则化网络、相关性向量机和其他。理论问题包括学习曲线和PAC-Bayesian框架，并讨论了几个用于学习大数据集的近似方法。书中包含了说明性的例子和练习，代码和数据集也可以在网上找到。附录提供了数学背景和对高斯马尔可夫过程的讨论。