Research Catalog
Gaussian processes for machine learning
- Title
- Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams.
- Author
- Rasmussen, Carl Edward.
- Publication
- Cambridge, Mass. : MIT Press, [2006], ©2006.
Items in the Library & Off-site
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1 Item
| Status | Format | Access | Call Number | Item Location |
|---|---|---|---|---|
Not available - Please for assistance. | Text | Use in library | QA274.4 .R37 2006 | Off-site |
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Details
- Additional Authors
- Williams, Christopher K. I.
- Description
- xviii, 248 pages : illustrations; 26 cm.
- 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."--BOOK JACKET.
- Series Statement
- Adaptive computation and machine learning
- Uniform Title
- Adaptive computation and machine learning.
- Subjects
- Bibliography (note)
- Includes bibliographical references (p. [223]-238) and indexes.
- Contents
- 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.
- ISBN
- 026218253X
- LCCN
- 2005053433
- OCLC
- ocm61285753
- SCSB-5237217
- Owning Institutions
- Columbia University Libraries