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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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, Tubingen. 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.
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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
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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
10 of 11 people found the following review helpful
Format:Hardcover
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.
Format:Hardcover
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.
Most Helpful Customer Reviews on Amazon.com (beta)
Amazon.com:
4.7 out of 5 stars
3 reviews
6 of 6 people found the following review helpful
5.0 out of 5 stars
Easily worth three times its price.
2 May 2012
By Steven Burns
- Published on Amazon.com
Format:Hardcover|Amazon 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.
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.
7 of 8 people found the following review helpful
5.0 out of 5 stars
Great, includes a good explanation of the connection between GP and SVM
23 Jun 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.
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.
4.0 out of 5 stars
More general than what title says
6 Jun 2013
By Ahmet Hungari
- Published on Amazon.com
Format:Hardcover|Amazon 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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4.7 out of 5 stars
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