Gaussian Processes for Machine Learning

MIT Press

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.

Autoren/Hrsg.

Rasmussen, Carl Edward

Williams, Christopher K. I.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Über Autor(innen)

Rasmussen, Carl Edward
Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University
of Cambridge, and Adjunct Research Scientist at the Max Planck Institute for
Biological Cybernetics, Tübingen.

Williams, Christopher K. I.
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.