Includes bibliographical references (pages 223-238) and indexes.
Print version record.
Regression -- Classification -- Covariance functions -- Model selection and adaptation of hyperparameters -- Relationships between GPs and other models -- Theoretical perspectives -- Approximation methods for large datasets -- Appendix A : Mathematical background -- Appendix B : Guassian Markov processes.
"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."--Jacket.