| Description |
1 online resource (xviii, 248 pages) : illustrations. |
| Physical Medium |
polychrome |
| Description |
text file |
| Series |
Adaptive computation and machine learning
|
|
Adaptive computation and machine learning.
|
| Summary |
"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. |
| Local Note |
MIT Press Direct MIT Press Direct Open Access |
| Subject |
Gaussian processes -- Data processing.
|
|
Gaussian processes -- Data processing. |
|
Gaussian processes. |
|
Machine learning -- Mathematical models.
|
|
Machine learning -- Mathematical models. |
|
Machine learning. |
| Indexed Term |
COMPUTER SCIENCE/Machine Learning & Neural Networks |
| Added Author |
Williams, Christopher K. I.
|
| ISBN |
9780262256834 (electronic book) |
|
0262256835 (electronic book) |
|
1423769902 (electronic book) |
|
9781423769903 (electronic book) |
|
9780262182539 |
|
026218253X |
|
