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Gaussian Processes for Machine Learning Hardcover – Nov 23 2005


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Product Details

  • Hardcover: 266 pages
  • Publisher: The MIT Press; 1 edition (Nov. 23 2005)
  • Language: English
  • ISBN-10: 026218253X
  • ISBN-13: 978-0262182539
  • Product Dimensions: 20.3 x 1.9 x 25.4 cm
  • Shipping Weight: 739 g
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: #75,001 in Books (See Top 100 in Books)

Product Description

About the Author

Carl Edward Rasmussen is a Lecturer at the Department of Engineering, University of Cambridge, and Adjunct Research Scientist at the Max Planck Institute for Biological Cybernetics, Tübingen.

Christopher K. I. Williams is Professor of Machine Learning and Director of the Institute for Adaptive and Neural Computation in the School of Informatics, University of Edinburgh.

Inside This Book (Learn More)
First Sentence
In this book we will be concerned with supervised learning, which is the problem of learning input-output mappings from empirical data (the training dataset). Read the first page
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Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most helpful customer reviews

By recursix on Jan. 10 2012
Format: Hardcover Verified Purchase
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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Most Helpful Customer Reviews on Amazon.com (beta)

Amazon.com: 3 reviews
12 of 13 people found the following review helpful
Great, includes a good explanation of the connection between GP and SVM June 22 2009
By Alexis J. Pribula - Published on Amazon.com
Format: Hardcover
A specific advantage of this book is that it is one of the few that dedicate a whole chapter on the connection between Bayesian methods using Gaussian Processes and Reproducing Kernel Hilbert Spaces. Even if this connection is a posteriori pretty obvious, it is nice to have it broken down clearly into small understandable pieces.

Otherwise, all the explanations concerning Gaussian Processes themselves for regression and classification are very clear and make this book a very worthwhile read. I would recommend also reading other books focusing more on Reproducing Kernel Hilbert Spaces in order to have a complete picture of these methods (e.g. "Learning with Kernels" by Scholkopf and Smola or for an even broader picture "Generalized Additive Models" by Hastie and Tibshirani).

Finally, since GP and RKHS for classification are still evolving subjects, it is probably a good idea to keep reading more material on them after finishing this book.
7 of 8 people found the following review helpful
Easily worth three times its price. May 2 2012
By Steven B. - Published on Amazon.com
Format: Hardcover Verified Purchase
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.
0 of 1 people found the following review helpful
More general than what title says June 6 2013
By Ahmet Hungari - Published on Amazon.com
Format: Hardcover Verified Purchase
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.


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