ISBN: 9780262182539
Carl Edward Rasmussen(Author); Christopher K. I. Williams(Author)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) [Hardcover] Rasmussen, Carl Edward and Williams, Christopher K. I.
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) [Hardcover] Rasmussen, Carl Edward and Williams, Christopher K. I.
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Author
Carl Edward Rasmussen(Author); Christopher K. I. Williams(Author)
Publisher
The MIT Press
Books
1 book
ISBN 13
9780262182539
About this edition
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.
More details
Publisher: The MIT Press
Language: English
Print length: 272 pages
Binding: Print length
Dimensions: 10.22 x 8.26 x 0.73 inches
Item weight: 1.62 pounds
Best Sellers Rank (Amazon): 606499
Language: English
Print length: 272 pages
Binding: Print length
Dimensions: 10.22 x 8.26 x 0.73 inches
Item weight: 1.62 pounds
Best Sellers Rank (Amazon): 606499
Shipping and returns
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If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
1. Return Window
- Eligible for return within 30 days of delivery.
128. Return Conditions
- The book must be brand new (unused, unmarked, and undamaged).
Important Notes:
If the returned book is damaged or missing components, the refund may be denied. If the book arrives damaged (e.g., due to shipping issues), a full refund will be issued. For returns due to non-quality issues (e.g., buyer’s change of mind), the customer must cover return shipping costs.
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The MIT Press
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) [Hardcover] Rasmussen, Carl Edward and Williams, Christopher K. I.
What's inside the box
Book 1
Gaussian Processes for Machine Learning
ISBN 9780262182539
About this collection
Gaussian Processes for Machine Learning provides a comprehensive and self-contained introduction to Gaussian processes as a principled probabilistic approach to learning with kernel machines.
Covering regression, classification, covariance functions, model selection, and practical algorithms, the book also explores connections with support-vector machines, neural networks, splines, and other machine learning methods.
Designed for researchers and students in machine learning and applied statistics, it combines theoretical foundations, practical examples, exercises, and approximation techniques for working with large datasets.
Over 5,000 pages of gripping storytelling in a stylish, display-worthy format.
Perfect for longtime fans or readers new to the series — the ultimate set for fantasy lovers or collectors looking to complete their shelves.
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