Product Description

This book presents 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 increasing 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, regularisation 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.

Product Details

• Amazon Sales Rank: #305220 in Books
• Published on: 2006-01-10
• Original language: English
• Number of items: 1
• Binding: Hardcover
• 266 pages

About the Author
Carl Edward Rasmussen is a Research Scientist at the Department of Empirical Inference for Machine Learning and Perception at the Max Planck Institute for Biological Cybernetics, Tubingen. Christopher K. I. Williams is Professor of Machine Learning and Director of the Institute for Adaptive and Neural Computation in the School of Informatics, University of Edinburgh.

Customer Reviews

Excellent overview
I run a company that specialises in the use of Gaussian Process type models for problem-solving in engineering and financial sectors. I was delighted when I stumbled upon this book as it collects a lot of the research I have done over the last 15 years from disparate places into one well thought out volume. For anyone considering the need to understand this area better I cannot think of any single book that I would recommend above this.