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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) Hardcover – November 23, 2005


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Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning series) + Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series) + Pattern Recognition and Machine Learning (Information Science and Statistics)
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Product Details

  • Series: Adaptive Computation and Machine Learning series
  • Hardcover: 266 pages
  • Publisher: The MIT Press (November 23, 2005)
  • Language: English
  • ISBN-10: 026218253X
  • ISBN-13: 978-0262182539
  • Product Dimensions: 8 x 0.8 x 10 inches
  • Shipping Weight: 1.6 pounds (View shipping rates and policies)
  • Average Customer Review: 4.7 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #168,847 in Books (See Top 100 in Books)

Editorial Reviews

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.

Customer Reviews

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Most Helpful Customer Reviews

12 of 13 people found the following review helpful By Alexis J. Pribula on June 22, 2009
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.
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7 of 8 people found the following review helpful By Steven B. on May 2, 2012
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.
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0 of 1 people found the following review helpful By Ahmet Hungari on June 6, 2013
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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