TY  - BOOK
AU  - Rasmussen,Carl Edward
AU  - Williams,Christopher K.I.
TI  - Gaussian processes for machine learning
T2  - Adaptive computation and machine learning
SN  - 026218253X
AV  - QA274.4 .R37 2006
U1  - 519.23 22
PY  - 2006///]
CY  - Cambridge, Mass.
PB  - MIT Press
KW  - Gaussian processes
KW  - Data processing
KW  - Machine learning
KW  - Mathematical models
N1  - Includes bibliographical references (pages 223-238) and index; 1; Introduction --; 2; Regression --; 3; Classification --; 4; Covariance functions --; 5; Model selection and adaptation of hyperparameters --; 6; Relationships between GPs and other models --; 7; Theoretical perspectives --; 8; Approximation methods for large datasets --; 9; Further issues and conclusions
N2  - "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."--BOOK JACKET
ER  -