Kernel Methods for Pattern Analysis [Hardcover]

John Shawe-Taylor (Author), Nello Cristianini (Author), Nello Cristianini (Author)

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Product Details

Hardcover: 476 pages
Publisher: Cambridge University Press; 1 edition (Jun 28 2004)
Language: English
ISBN-10: 0521813972
ISBN-13: 978-0521813976
Product Dimensions: 25.5 x 17.4 x 2.7 cm
Shipping Weight: 1 Kg
Amazon Bestsellers Rank: #412,522 in Books (See Top 100 in Books)
- #40 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Human Vision & Language Systems
- #47 in Books > Science > Biological Sciences > Bioinformatics
- #58 in Books > Computers & Internet > Computer Science > Artificial Intelligence > Machine Learning

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Product Description

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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Inside This Book (Learn More)

First Sentence
Pattern analysis deals with the automatic detection of patterns in data. Read the first page

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Concordance

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Most Helpful Customer Reviews on Amazon.com ^(beta)

Amazon.com: 4.1 out of 5 stars (8 customer reviews)

28 of 33 people found the following review helpful

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

By machine learning guru - Published on Amazon.com

This review is from: Kernel Methods for Pattern Analysis (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.

20 of 24 people found the following review helpful

2.0 out of 5 stars Nice introduction, but no more, Aug 11 2006

By gilgamash - Published on Amazon.com

This review is from: Kernel Methods for Pattern Analysis (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.

23 of 29 people found the following review helpful

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

By Data Miner - Published on Amazon.com

This review is from: Kernel Methods for Pattern Analysis (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.

› Go to Amazon.com to see all 8 reviews 4.1 out of 5 stars