eBook
Gaussian Processes for Machine Learning
| Τίτλος: | Gaussian Processes for Machine Learning |
|---|---|
| Περιγραφή: | 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. |
| Συγγραφείς: | Carl Edward Rasmussen, Christopher K. I. Williams |
| Resource Type: | eBook. |
| Θέματα: | Machine learning--Mathematical models, Gaussian processes--Data processing |
| Categories: | COMPUTERS / Computer Science, COMPUTERS / Artificial Intelligence / General |
| Βάση Δεδομένων: | eBook Index |
| FullText | Text: Availability: 0 |
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| Header | DbId: edsebk DbLabel: eBook Index An: 156015 RelevancyScore: 862 AccessLevel: 6 PubType: eBook PubTypeId: ebook PreciseRelevancyScore: 861.970825195313 |
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| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=156015 |
| RecordInfo | BibRecord: BibEntity: Classifications: – Code: 519.23 Scheme: ddc Type: prePub Languages: – Code: eng Text: English Subjects: – SubjectFull: Machine learning--Mathematical models Type: general – SubjectFull: Gaussian processes--Data processing Type: general Titles: – TitleFull: Gaussian Processes for Machine Learning Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Carl Edward Rasmussen – PersonEntity: Name: NameFull: Christopher K. I. Williams – PersonEntity: Name: NameFull: Carl Edward Rasmussen – PersonEntity: Name: NameFull: Christopher K. I. Williams IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Type: published Y: 2006 – D: 04 M: 02 Type: profile Y: 2014 Identifiers: – Type: isbn-print Value: 9780262182539 – Type: isbn-electronic Value: 9780262256834 Titles: – TitleFull: Gaussian Processes for Machine Learning Type: main |
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