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Carl Edward Rasmussen

Gaussian Processes for Machine Learning Hardcover – 10 Jan. 2006

by Carl Edward Rasmussen (Author), Christopher K. I. Williams (Author)

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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.

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Product details

Hardcover: 272 pages
Publisher: MIT Press (10 Jan. 2006)
Language: English
ISBN-10: 9780262182539
ISBN-13: 978-0262182539
ASIN: 026218253X
Product Dimensions: 26 x 21 x 1.9 cm
Customer reviews: 20 customer ratings
Amazon Bestsellers Rank: 86,131 in Books (See Top 100 in Books)
- #109 in Applied Mathematics (Books)
- #69 in Computer Architecture & Microprocessors
- #46 in Algorithmic Programming

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This book presents 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 increasing 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, regularisation 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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Customer reviews

5 star		72%
4 star		19%
3 star 0% (0%)		0%
2 star		9%
1 star 0% (0%)		0%

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David

Great condition

Reviewed in the United Kingdom on 30 November 2018

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Arrived in great condition.

One person found this helpful

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Four Stars

Reviewed in the United Kingdom on 1 May 2017

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Good book.

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Dr. C

Excellent software support (MATLAB

Reviewed in the United Kingdom on 3 December 2015

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Essential read. Excellent software support (MATLAB.) Thank you.

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Magica

Five Stars

Reviewed in the United Kingdom on 27 June 2014

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it looks very good!!!

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Robert

I bought this book expecting a reference in the domain ...

Reviewed in the United Kingdom on 19 August 2016

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I bought this book expecting a reference in the domain of ML. I found it to be light in content and depth (the book is actually very thin).

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Jefferson

Four Stars

Reviewed in the United Kingdom on 2 June 2016

Very good textbook, just the right amount of theory and clearly explained.

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Michael Hopkins

Excellent overview

Reviewed in the United Kingdom on 11 April 2009

I run a company that specialises in the use of Gaussian Process type models for problem-solving in engineering and financial sectors. I was delighted when I stumbled upon this book as it collects a lot of the research I have done over the last 15 years from disparate places into one well thought out volume. For anyone considering the need to understand this area better I cannot think of any single book that I would recommend above this.

12 people found this helpful

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A happy reader

"The bible of Gaussian Processes."

Reviewed in the United Kingdom on 30 April 2012

This book is well-written, has a good content structure and has a balanced coverage of theory and applications in each chapter. It offers an accessible yet quite complete treatment of the Gaussian Processes framework and can also act as a good "standard reference". If you are interested in Gaussian Processes you should definitely have a look at it.

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JesusPresedo

Referencia obligada en Procesos Gausianos

Reviewed in Spain on 19 February 2018

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Un libro muy bueno aunque algo duro de leer. Me gusta su carácter de tipo OpenSource (está disponible en la red el pdf del libro y el código fuente de programas que implementan los algoritmos de dicho libro).

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Cliente de Amazon

Buen contenido pero mal empaquetado.

Reviewed in Mexico on 9 March 2020

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No vino empaquetado como esperaba. Sobretodo tomando en cuenta que es un envío internacional. No está terrible pero tenía una manchita y las orillas estaban tantito dobladas.

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recursix

Excellent book

Reviewed in Canada on 10 January 2012

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Simple and pedagogical book covering a great Non-Parametric Bayesian approach for kernek methods. Go and read the 4 first chapters. This will add a great tool to your machine learning tool box.

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Client d'Amazon

Nice book!!

Reviewed in France on 29 December 2016

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I wanted this book for a long time!!
- The quality of the product is perfect (Hardcover),
- The packaging of the product was proper,
- I received the book the day after the purchase,
- Also, the price was great!!

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Excellent book on GP

Reviewed in Canada on 8 June 2015

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Excellent book on GP

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Steven B.

Easily worth three times its price.

Reviewed in the United States on 2 May 2012

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Even though this is not a cookbook on Gaussian Processes, the explanations are clear and to the point.

The book is highly technical but it also does a great job explaining how Gaussian Processes fit in the big picture regarding the last few decades in the Machine Learning field and how they are related in some ways to both SVM and Neural Networks.

I'm still working my way through the book but so far I'm extremely pleased with it. As the first reviewer said, it's an evolving subject so keep looking for new material.

It's a well-edited hardcover book and at this price it's a steal.

14 people found this helpful

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Seonwoo Lee

Good

Reviewed in the United States on 6 June 2019

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Good!

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Birkan Tunc

More general than what title says

Reviewed in the United States on 6 June 2013

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This is another great book on ML. Although title suggests that it is solely about GP, author manages to include a lot on general ML in such a small volume (but, yes it is mostly about GP). If you are already familiar with basics of ML, this book may help you understand some details. And, of course GP techniques produce really nice plots; even this fact alone is enough to try.

7 people found this helpful

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Gaussian Processes for Machine Learning Hardcover – 10 Jan. 2006

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