Gaussian Processes for Machine Learning
A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received incr...
Furkejuvvon:
| Váldodahkkit: | , |
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| Materiálatiipa: | Online |
| Giella: | eaŋgalasgiella |
| Almmustuhtton: |
The MIT Press
2022
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| Fáttát: | |
| Liŋkkat: | ONIX_20220221_9780262256834_4 |
| Fáddágilkorat: |
Eai fáddágilkorat, Lasit vuosttaš fáddágilkora!
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| _version_ | 1869522802656149504 |
|---|---|
| author | Rasmussen, Carl Edward Williams, Christopher K. I. |
| author_browse | Rasmussen, Carl Edward Williams, Christopher K. I. |
| author_facet | Rasmussen, Carl Edward Williams, Christopher K. I. |
| author_sort | Rasmussen, Carl Edward |
| collection | Directory of Open Access Books |
| description | A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 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. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. |
| format | Online |
| id | doab-20.500.12854ir-78483 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | The MIT Press |
| publisherStr | The MIT Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-784832024-04-14T10:28:08Z Gaussian Processes for Machine Learning Rasmussen, Carl Edward Williams, Christopher K. I. Computer science Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning A comprehensive and self-contained introduction to Gaussian processes, which provide a principled, practical, probabilistic approach to learning in kernel machines. 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. The book deals with the supervised-learning problem for both regression and classification, and includes detailed algorithms. A wide variety of covariance (kernel) functions are presented and their properties discussed. Model selection is discussed both from a Bayesian and a classical perspective. Many connections to other well-known techniques from machine learning and statistics are discussed, including support-vector machines, neural networks, splines, regularization networks, relevance vector machines and others. Theoretical issues including learning curves and the PAC-Bayesian framework are treated, and several approximation methods for learning with large datasets are discussed. The book contains illustrative examples and exercises, and code and datasets are available on the Web. Appendixes provide mathematical background and a discussion of Gaussian Markov processes. 2022-02-21T15:09:33Z 2022-02-21T15:09:33Z 2005 book ONIX_20220221_9780262256834_4 9780262256834 9780262182539 https://directory.doabooks.org/handle/20.500.12854/78483 eng Adaptive Computation and Machine Learning series image/jpeg n/a https://doi.org/10.7551/mitpress/3206.001.0001 The MIT Press The MIT Press 10.7551/mitpress/3206.001.0001 10.7551/mitpress/3206.001.0001 ae0cf962-f685-4933-93d1-916defa5123d 9780262256834 9780262182539 The MIT Press 272 Cambridge open access |
| spellingShingle | Computer science Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning Rasmussen, Carl Edward Williams, Christopher K. I. Gaussian Processes for Machine Learning |
| title | Gaussian Processes for Machine Learning |
| title_full | Gaussian Processes for Machine Learning |
| title_fullStr | Gaussian Processes for Machine Learning |
| title_full_unstemmed | Gaussian Processes for Machine Learning |
| title_short | Gaussian Processes for Machine Learning |
| title_sort | gaussian processes for machine learning |
| topic | Computer science Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| topic_facet | Computer science Artificial intelligence thema EDItEUR::U Computing and Information Technology::UY Computer science thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning |
| url | ONIX_20220221_9780262256834_4 |
| work_keys_str_mv | AT rasmussencarledward gaussianprocessesformachinelearning AT williamschristopherki gaussianprocessesformachinelearning |