Gaussian Processes for Machine Learning
Overview & Synopsis
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.
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Document Information
| Document Title | Gaussian Processes for Machine Learning |
|---|---|
| Author / Organization | Carl Edward Rasmussen, Christopher K. I. Williams |
| Subject & Discipline | Machine Learning, Probability and Stochastic Processes, Statistics and SAS Programming, Artificial Intelligence, Data Analysis and Data Mining, Neural Networks |
| Categories | Machine Learning |
| Publication Year | 2005 |
| ISBN | 026218253X |
| Resource Verification | ✓ Verified Direct PDF Available |
| Alternative Mirrors & Links | |
| Original Host Website |
FreeComputerBooks.com
(freecomputerbooks.com)
|
| File Format | application/pdf (Portable Document Format) |
| Reported File Size | 3957.3 KB (4,052,292 bytes) |
| Language | ENGLISH |
| Catalog ID | DOC-10574 |
| Original Source Link | Visit Source Page on freecomputerbooks.com » |
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