Kernel Methods for Pattern Analysis (Hardcover)

by John Shawe-Taylor (Author), Nello Cristianini (Author) "Pattern analysis deals with the automatic detection of patterns in data..." (more)
Key Phrases: subsequences kernel, hard margin support vector machine, subtree kernel, Code Fragment, Gram Schmidt, Proof Consider (more...)

Editorial Reviews

Review
"The book provides an excellent overview of this growing field. I highly recommend it to those who are interested in pattern analysis and machine learning, and especially to those who want to apply kernel-based methods to text analysis and bioinformatics problems."
Computing Reviews

"I enjoyed reading this book and am happy about its addition to my library as it is a valuable practitioner's reference. I especially liked the presentation of kernel-based pattern analysis algorithms in terse mathematical steps clearly identifying input data, output data, and steps of the process. The accompanying Matlab code or pseudocode is also extremely useful."
IAPR Newsletter

"If you are interested in an introduction to statistical techniques for analyzing text documents, Kernel Methods will serve you well."
M. Last, Journal of the American Statistical Association

Product Description
This book provides professionals with a large selection of algorithms, kernels and solutions ready for implementation and suitable for standard pattern discovery problems in fields such as bioinformatics, text analysis and image analysis. It also serves as an introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.

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Product Details

• Hardcover: 476 pages
• Publisher: Cambridge University Press; illustrated edition edition (June 28, 2004)
• Language: English
• ISBN-10: 0521813972
• ISBN-13: 978-0521813976
• Product Dimensions: 9.8 x 7 x 1.1 inches
• Shipping Weight: 2.2 pounds (View shipping rates and policies)
• Average Customer Review: 3.4 out of 5 stars See all reviews (7 customer reviews)
• Amazon.com Sales Rank: #644,846 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

26 of 29 people found the following review helpful:

5.0 out of 5 stars coherent and accessible reference, ready-to-use algorithms, February 21, 2005

By	machine learning guru - See all my reviews

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.

23 of 27 people found the following review helpful:

5.0 out of 5 stars A Useful Reference on Kernel Methods, February 21, 2005

By	Data Miner - See all my reviews

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.

19 of 22 people found the following review helpful:

5.0 out of 5 stars THE reference work, February 21, 2005

By	Tijl De Bie "tijl" (belgium) - See all my reviews (REAL NAME)

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.