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Kernel Methods for Pattern Analysis 1st Edition

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John Shawe-Taylor (Author)
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John Shawe-Taylor (Author), Nello Cristianini (Author)
4.1 out of 5 stars 8 customer reviews
ISBN-13: 978-0521813976
ISBN-10: 0521813972
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Frequently Bought Together

  • Kernel Methods for Pattern Analysis
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  • Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning)
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  • An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
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Product Details

  • Hardcover: 478 pages
  • Publisher: Cambridge University Press; 1 edition (June 28, 2004)
  • Language: English
  • ISBN-10: 0521813972
  • ISBN-13: 978-0521813976
  • Product Dimensions: 6.8 x 1.1 x 9.7 inches
  • Shipping Weight: 2.2 pounds (View shipping rates and policies)
  • Average Customer Review: 4.1 out of 5 stars  See all reviews (8 customer reviews)
  • Amazon Best Sellers Rank: #371,851 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

5.0 out of 5 starscoherent and accessible reference, ready-to-use algorithms
By machine learning guru on February 21, 2005
Format: Hardcover
This work presents a coherent overview of an important field in machine learning. The unifying framework of kernel methods has proven state of the art results and the community has been waiting for a book like this to make both theory and practice of kernel methods accesssible for readers of all different backgrounds (researchers, students, practioners from both academia and industry, ...).

It is theoretically well-founded, the resulting algorithms are well-explained and made accessible for practioners by providing pseudo-code and online, ready-to-use matlab code.

This book nicely complements the previous, yellow book, written by the same authors. Indeed, after "getting into the field" by reading the accessible introduction to support vector machines (SVMs), it was clear to me that SVMs was only an example of a signifcantly larger framework, i.e., kernel methods. The blue book is the reference book about that larger framework I have been waiting for since then. I particularly like the way the book is set up, making clear the modular, flexible approach in kernel methods.
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2.0 out of 5 starsNice introduction, but no more
By gilgamash on August 11, 2006
Format: Hardcover
Well, at first I was petrified to find a book that sounded like it deeply explores the subject of kernel methods. But all in all, it did not quite achieve what I hoped for. As a practical approach, when it comes to implementation, it serves nicely as a reference. The deeper mathematical roots of kernels (especially when it comes to measure theory and functional analysis) are not dealt with at all or just scratched at the very surface. The notation is sometimes awkward, mentioning for example the representation of an object in a given vector space with respect to the basis. And: Too much copied and pasted from the former book about SVMs. Basically, reading papers of Carmeli, Aronszajn and others will give you a much deeper insight into the subject.
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5.0 out of 5 starsA Useful Reference on Kernel Methods
By Data Miner on February 21, 2005
Format: Hardcover
The book is divided into 3 parts. The theory is all in part I,

the rest of the book is a cook-book with plenty of matlab code.

The website contains most of the same code + data online. Readable, complete.
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5.0 out of 5 starsTHE reference work
By Tijl De Bie on February 21, 2005
Format: Hardcover
This book will, without doubt, become THE reference work in kernel methods for pattern recognition, and a must read for pattern recognition researchers and practitioners in general.

It's built up in a nicely modular, accessible and didactive way, helping the reader understand thoroughly what kernel methods are all about and importantly, how to use them. This makes the book very useful as a cook book for practitioners, as well as a text book for students.

The book covers all the relevant topics in the state of the art of kernel methods, a field of research in which the authors have been a driving force since the beginning. Even so, they managed to resist the temptation from squeezing in the(ir) latest (potentially still unstable) results, which greatly enhances the timelessness of the book.
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