Statistical Pattern Recognition, 2nd Edition [Hardcover]

Andrew R. Webb (Author)

Book Description

Publication Date: October 15, 2002 | ISBN-10: 0470845139 | ISBN-13: 978-0470845134 | Edition: 2

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.

Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.

* Provides a self-contained introduction to statistical pattern recognition.
* Each technique described is illustrated by real examples.
* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.
* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.
* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.
* Features a variety of exercises, from 'open-book' questions to more lengthy projects.

The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

For further information on the techniques and applications discussed in this book please visit www.statistical-pattern-recognition.net

Editorial Reviews

Review

"...an excellent self-contained introductory text..." (Technometrics, Vol. 45, No. 4, November 2003)

From the Back Cover

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition.

Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides a comprehensive introduction to this vibrant area - with material drawn from engineering, statistics, computer science and the social sciences - and covers many application areas, such as database design, artificial neural networks, and decision support systems.
* Provides a self-contained introduction to statistical pattern recognition.

* Each technique described is illustrated by real examples.

* Covers Bayesian methods, neural networks, support vector machines, and unsupervised classification.

* Each section concludes with a description of the applications that have been addressed and with further developments of the theory.

* Includes background material on dissimilarity, parameter estimation, data, linear algebra and probability.

* Features a variety of exercises, from 'open-book' questions to more lengthy projects.
The book is aimed primarily at senior undergraduate and graduate students studying statistical pattern recognition, pattern processing, neural networks, and data mining, in both statistics and engineering departments. It is also an excellent source of reference for technical professionals working in advanced information development environments.

See all Editorial Reviews

Product Details

• Hardcover: 534 pages
• Publisher: Wiley; 2 edition (October 15, 2002)
• Language: English
• ISBN-10: 0470845139
• ISBN-13: 978-0470845134
• Product Dimensions: 9.7 x 7.1 x 1.4 inches
• Shipping Weight: 2.4 pounds (View shipping rates and policies)
• Average Customer Review: 3.3 out of 5 stars  See all reviews (3 customer reviews)
• Amazon Best Sellers Rank: #3,395,547 in Books (See Top 100 in Books)

More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

Most Helpful Customer Reviews

3 of 3 people found the following review helpful:

4.0 out of 5 stars The most comprehensive book about machine learning, February 21, 2008

By 

Sandro Saitta "www.dataminingblog.com" (Switzerland) - See all my reviews
(REAL NAME)   

Amazon Verified Purchase

This review is from: Statistical Pattern Recognition, 2nd Edition (Paperback)

The book written by Andrew Webb is certainly the most comprehensive book related to machine learning. I have not been able to find any machine learning topic which is not treated in this book.

According to me, this book is more for a scientific audience for the simplest reason that the presentation gives more importance to equations than to application examples. It does not explain how to program machine learning algorithm but rather which algorithms exist and what is their mathematical background. Every technique is presented first using text and only then mathematical development is shown. Therefore, it is convenient for people preferring textual description as well as the ones preferring equations.

The book is very well structured. Every chapter starts with a textual introduction on the related issue and then describes several techniques to solve it. At the end, specific application examples are given. A large part is then devoted to summary, discussion, recommendations (not always), notes and references, and finally exercises. Topics are covered in a non standard way for people used to data mining practical books. After an introduction, density estimation techniques are explained. Then linear and non-linear discriminant analyzes. It goes on with decision trees, performance and feature selection to finish with clustering and some other additional topics. Although this book is written in a statistical point of view, it is certainly one of the most comprehensive resource for machine learning and data mining.

7 of 20 people found the following review helpful:

1.0 out of 5 stars Very Bad treatment of the subject, January 12, 2006

By 

sarfraz saqib "saqib" (germany) - See all my reviews

This review is from: Statistical Pattern Recognition, 2nd Edition (Paperback)

The author claims that this book is written for senior undergrads and gaduate students, on the contraray, of what he claimed, his treatment of the suject is very sketchy. He has written this book in a somewhat citational manner i.e not treating any details of the concerned topics whatsoever and only stating the facts directly like he is citing some kind of terminolgy and not intersted in giving the reader a thourough understanding of the subject.

He has given extensive references and urls and so this book is more like " I can't explain anything go search urself here".

I think its the most worst way anybody could adopt for writing a book. In my opinion the only purpose of this book was to have a publication on his credit.

I would strongly recommend any students to refrain from buying this one as it will not help you much in any way.

Or else if u realy like to use very expensive toilet paper then give this book a try.

7 of 28 people found the following review helpful:

5.0 out of 5 stars This book is good guidance., November 3, 2000

By A Customer

This review is from: Statistical Pattern Recognition (Paperback)

I recently started study about Pattern Recognition. This book is so well organized.

- Introduction to statistical pattern recognition

- Basic approaches to supervised classification via Bayes' rule and estimation of the class-conditional densities.

- Discriminant function approach to supervised classification.

- Techniques of exploratory data analysis.

- Additional topics on pattern recognition including performance assessment.

Especially, this book contains URL which concerned with topics. It is very useful!!