Bibliograph

Gaussian processes for machine learning, Carl Edward Rasmussen, Christopher K.I. Williams

Type

Label

Gaussian processes for machine learning, Carl Edward Rasmussen, Christopher K.I. Williams

Language

eng

Bibliography note

Includes bibliographical references (pages 223-238) and indexes

Illustrations

illustrations

Index

index present

Literary Form

non fiction

Main title

Gaussian processes for machine learning

Nature of contents

bibliographydictionaries

Oclc number

68194203

Responsibility statement

Carl Edward Rasmussen, Christopher K.I. Williams

Review

"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

Series statement

Adaptive computation and machine learning

Table Of Contents

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

Classification