Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning Series)

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9780262182539: Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning Series)

Book by Rasmussen Carl Edward Williams Christopher K I

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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.

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Carl Edward Rasmussen, Christopher K. I. Williams
Editorial: MIT Press Ltd, United States (2006)
ISBN 10: 026218253X ISBN 13: 9780262182539
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Descripción MIT Press Ltd, United States, 2006. Hardback. Estado de conservación: New. 254 x 206 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AAU9780262182539

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Carl Edward Rasmussen
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Descripción MIT Press, 2006. HRD. Estado de conservación: New. New Book. Shipped from UK in 4 to 14 days. Established seller since 2000. Nº de ref. de la librería BB-9780262182539

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Carl Edward Rasmussen, Christopher K. I. Williams
Editorial: MIT Press Ltd, United States (2006)
ISBN 10: 026218253X ISBN 13: 9780262182539
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Descripción MIT Press Ltd, United States, 2006. Hardback. Estado de conservación: New. 254 x 206 mm. Language: English . Brand New Book. 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. Nº de ref. de la librería AAU9780262182539

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Descripción Estado de conservación: New. Depending on your location, this item may ship from the US or UK. Nº de ref. de la librería 97802621825390000000

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Rasmussen, Carl Edward/ Williams, Christopher K. I.
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Descripción MIT Press 2006-01-10, 2006. HARDCOVER. Estado de conservación: New. Nº de ref. de la librería NU-GRD-00624049

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Carl Edward Rasmussen, Christopher K I Williams
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ISBN 10: 026218253X ISBN 13: 9780262182539
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Descripción MIT 2006-01-10, Cambridge, Mass. |London, 2006. hardback. Estado de conservación: New. Nº de ref. de la librería 9780262182539

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Descripción MIT Press Ltd. Hardback. Estado de conservación: new. BRAND NEW, Gaussian Processes for Machine Learning, Carl Edward Rasmussen, Christopher K. I. Williams, 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. Nº de ref. de la librería B9780262182539

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Descripción Mit University Press Group Ltd, 2006. Estado de conservación: New. Nº de ref. de la librería EH9780262182539

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Descripción MIT Press. Estado de conservación: New. Brand New. Nº de ref. de la librería 026218253X

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