Gaussian processes for machine learning / Carl Edward Rasmussen, Christopher K.I. Williams.
Material type: TextSeries: Adaptive computation and machine learningPublisher: Cambridge, Mass. : MIT Press, [2006]Copyright date: ©2006Description: xviii, 248 pages : illustrations ; 26 cmContent type:- text
- unmediated
- volume
- 026218253X
- 9780262182539
- 519.23 22
- QA274.4 .R37 2006
Item type | Current library | Call number | Copy number | Status | Date due | Barcode | |
---|---|---|---|---|---|---|---|
Book | City Campus City Campus Main Collection | 519.23 RAS (Browse shelf(Opens below)) | 1 | Available | A402039B |
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519.23 LIP Statistics of random processes / | 519.23 LIP Statistics of random processes / | 519.23 OLO Probability, statistics, and stochastic processes / | 519.23 RAS Gaussian processes for machine learning / | 519.23 SOB Stochastic modeling of microstructures / | 519.23 STI Stochastic processes and models / | 519.23 STO Stochastic volatility : selected readings / |
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.
"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.
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