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Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach
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Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach:
Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - edition reliée, livre de poche

ISBN: 026218253X

[SR: 452185], Hardcover, [EAN: 9780262182539], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, 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., 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 280292, Machine Theory, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach
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Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach:
Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - edition reliée, livre de poche

ISBN: 026218253X

[SR: 263819], Hardcover, [EAN: 9780262182539], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, 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., 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 280292, Machine Theory, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach
Livre non disponible
(*)
Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach:
Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - edition reliée, livre de poche

ISBN: 026218253X

[SR: 452185], Hardcover, [EAN: 9780262182539], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, 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., 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 280292, Machine Theory, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach
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Carl Edward Rasmussen, Christopher K. I. Williams, Francis Bach:
Gaussian Processes for Machine Learning (Adaptive Computation And Machine Learning) - edition reliée, livre de poche

ISBN: 026218253X

[SR: 263819], Hardcover, [EAN: 9780262182539], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, 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., 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 280292, Machine Theory, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) - Carl Edward Rasmussen, Christopher K. I. Williams
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Carl Edward Rasmussen, Christopher K. I. Williams:
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) - edition reliée, livre de poche

ISBN: 026218253X

[SR: 297662], Hardcover, [EAN: 9780262182539], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, 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., 280291, Intelligence & Semantics, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 280292, Machine Theory, 3887, AI & Machine Learning, 3508, Computer Science, 5, Computers & Technology, 1000, Subjects, 283155, Books, 468204, Computer Science, 491298, Algorithms, 491300, Artificial Intelligence, 491306, Database Storage & Design, 491308, Graphics & Visualization, 491302, Networking, 491310, Object-Oriented Software Design, 491312, Operating Systems, 491314, Programming Languages, 491316, Software Design & Engineering, 465600, New, Used & Rental Textbooks, 2349030011, Specialty Boutique, 283155, Books

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Détails sur le livre
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.

Informations détaillées sur le livre - Gaussian Processes for Machine Learning


EAN (ISBN-13): 9780262182539
ISBN (ISBN-10): 026218253X
Version reliée
Livre de poche
Date de parution: 2005
Editeur: MIT PR
266 Pages
Poids: 0,748 kg
Langue: eng/Englisch

Livre dans la base de données depuis 29.05.2007 03:44:50
Livre trouvé récemment le 31.07.2018 23:42:51
ISBN/EAN: 9780262182539

ISBN - Autres types d'écriture:
0-262-18253-X, 978-0-262-18253-9


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