Pattern Classification (2nd Edition)

Book Description

The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available from the Wiley editorial department.

Customer Reviews:

Great product & service.......2007-09-21

This was my first purchase from amazon and I was totally impressed by the quality of the product and the service! I would buy again from the same seller and recommend others to do the same.

A Very Bad Sequel.......2007-03-09

I have now used this book 3 times for a class. While the 1st edition did a nice job of covering the material in its time, the additions to in the 2nd addition are a disaster. What the book has going for it is that it at least lists the necessary material for such a course in the table of contents. However, all the additional material is poorly explained at best. The problem sets are too few and the ones that are included are generally weak.

I have tried to use this book, but after constant student complaints and my own difficulty with the text, I have finally concluded that the problem lies with the text and not with the users.

I think an indicator of problems was the large number of errors in the first printing; large here is an understatement. Even in later additions, the 4th, the size of the errata is huge. I think this is indicative of the authors' attention to detail and seriousness in preparation. I have found similar errors and ambiguities in the associate Computer Manual.

The bottom line is that this book has seen its final appearance in our curriculum. I would use any other text, even an older one.

There is simply not enough room or time to point out all the problems with this text. Do yourself a favor if considering this text for a class. Don't bother.

The best book for the discussed field.......2007-02-05

The discussed book is very explanatory and could be students' material for academic lessons.

great book.......2007-01-16

easy to read for computer scientists who are not necessarily experts in statistics. the code in matlab is very good, and helps a lot.
this book is a good introduction to machine learning.

Very well written.......2006-02-26

I liked this book because it does a great job explaining the concepts and the reasoning behind the mathematical formulae. Other books such as "The Elements of Statistical Learning" toss the Math formulas at you and expect you to figure out the significance or the importance of 'em. The book does not shy away from Math - but does a great job presenting it.

Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series)

Average customer rating:

A best book on Statistical Pattern Recognition
Standard reference and a classic text but with flaws
good coverage for engineers
Standard Reference in the Field

Introduction to Statistical Pattern Recognition, Second Edition (Computer Science and Scientific Computing Series)
Keinosuke Fukunaga
Manufacturer: Academic Press
ProductGroup: Book
Binding: Hardcover

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Pattern Classification (2nd Edition)
Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
Pattern Recognition, Third Edition
Pattern Recognition and Machine Learning (Information Science and Statistics)
Statistical Pattern Recognition, 2nd Edition

ASIN: 0122698517

Book Description

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern recognition and as a reference book for workers in the field. Each chapter contains computer projects as well as exercises.

Customer Reviews:

A best book on Statistical Pattern Recognition.......2005-09-13

Multivariate analysis is borrowed to name a NEW subject, Statistical Pattern Recognition (SPR). Many statisticians think it unfair or a shame. In spite of these, it is a good reference book of SPR. :-)

[1] Many contents of this book can be found in any graduate textbook of Multivariate Analysis, for instance, Fisher's linear disciminant, etc.
[2] The book is badly printed. Why not using LaTeX?
[3] Guassian distribution is assumed here and there.
[4] It may be good as a reference book, but definitely not as a textbook.

Standard reference and a classic text but with flaws.......2004-01-20

I do not like to consult this book for the following, quite superficial reason. The book is sloppily produced and proofread
(and the fault is [probably] mainly the publisher's instead of the author's). This manifests itself, e.g., as follows

(1) the typography is flawed (the equations hurt at least my eyes);
(2) at its each appearance, the all-important > < -sign goes the wrong way.

good coverage for engineers.......2000-08-04

Fukunaga is a standard source for pattern recognition methods often cited in the engineering literature. Covers parametric (particularly linear and quadratic discriminant algorithms) and nonparametric methods (density estimation). It is designed for and popular with engineers. When I was working at Nichols Research Corporation Fukunaga's papers and this book (earlier edition) were often cited as sources to justify the algorithms we used for discrimination problems. In fact Fukunaga had been a consultant to the company (used primarily by the Boston branch of the company where the KENN algorithms were developed). It is a reputable source. I still like Duda and Hart (1972) for good explanations of the fundamental concepts. For statisticians McLachlan's book is now far and away the best source.

Standard Reference in the Field.......2000-04-06

If you are writing a machine learning paper, and need to cite something to support an argument, you can almost always cite Fukunaga. This work is a standard reference in the field. The presentation of most material is very terse, but that is great if you already have a good feel for the material and need to look up some details about some algorithm or technique. There isn't much about neural networks here, but for the rest of the pattern recognition techniques, this is almost always the first place to start. Another strong point for this book is the use of realistic examples, which illustrate many of the statistical techniques.

Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition

Average customer rating:

Underwhelmed
Excellent toolbox to learn & use.

Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
David G. Stork , and Elad Yom-Tov
Manufacturer: Wiley-Interscience
ProductGroup: Book
Binding: Paperback

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Similar Items:

Pattern Classification (2nd Edition)
Pattern Recognition and Machine Learning (Information Science and Statistics)
The Elements of Statistical Learning
Neural Networks for Pattern Recognition
Digital Image Processing Using MATLAB

ASIN: 0471429775

Book Description

Computer Manual to Accompany Pattern Classification and its associated MATLAB software is an excellent companion to Duda: Pattern Classfication, 2nd ed, (DH&S). The code contains all algorithms described in Duda as well as supporting algorithms for data generation and visualization. The Manual uses the same terminology as the DH&S text and contains step-by-step worked examples, including many of the examples and figures in the textbook.
The Manual is accompanied by software that is available electronically. The software contains all algorithms in DH&S, indexed to the textbook, and uses symbols and notation as close as possible to the textbook. The code is self-annotating so the user can easily navigate, understand and modify the code.

Customer Reviews:

Underwhelmed.......2007-04-04

Talk about over-hype from reviewer #1!

This "manual" is thin on substantive content, with TONS of whitespace & whitepages to stretch it out to ~125pages. The documentation of the code should be available as a PDF with files on MATLAB's file exchange or on the publisher's website. Save yourself some $$.

Excellent toolbox to learn & use........2004-07-09

I was one of the early access recipient of this toolbox and found it extremely useful. It basically has a whole bunch of cleaning and classification algos.

The toolbox also allows one to extend its use with new algorithms, tweaks or to use our dataset. As long as its formatted in the same fashion.

I would strongly recommend this toolbox, if you are looking for additional material, another book worth having is Christopher Bishop's book.

Cvpr 2001: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 8-14 December 2001 Kauai, Hawaii USA
Average customer rating:
Cvpr 2001: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 8-14 December 2001 Kauai, Hawaii USA

Manufacturer: Ieee
ProductGroup: Book
Binding: Paperback

Machine Vision | Artificial Intelligence | Computer Science | Computers & Internet | Subjects | Books
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ASIN: 0769512720

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) (The Morgan Kaufmann Series in Computer Graphics)
Average customer rating:

Good compendium

HDR - State of the Art

Not for the artist or photographer

A great resource

High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) (The Morgan Kaufmann Series in Computer Graphics)
Erik Reinhard , Greg Ward , Sumanta Pattanaik , and Paul Debevec
Manufacturer: Morgan Kaufmann
ProductGroup: Book
Binding: Hardcover

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ShaderX 4 Advanced Rendering Techniques (Graphics Series)

The HDRI Handbook: High Dynamic Range Imaging for Photographers and CG Artists

Advanced Global Illumination

Physically Based Rendering : From Theory to Implementation (The Morgan Kaufmann Series in Interactive 3D Technology) (The Interactive 3d Technology Series)

Physics Based Animation (Graphics Series)

ASIN: 0125852630
Release Date: 2005-08-24

Book Description

High dynamic range imaging produces images with a much greater range of light and color than conventional imaging. The effect is stunning, as great as the difference between black-and-white and color television. High Dynamic Range Imaging is the first book to describe this exciting new field that is transforming the media and entertainment industries. Written by the foremost researchers in HDRI, it will explain and define this new technology for anyone who works with images, whether it is for computer graphics, film, video, photography, or lighting design.

* Written by the leading researchers in HDRI
* Covers all the areas of high dynamic range imaging including capture devices, display devices, file formats, dynamic range reduction, and image-based lighting
* Includes a DVD with over 4 GB of HDR images as well as source code and binaries for numerous tone reproduction operators for Windows, Linux, and Mac OS X

Customer Reviews:

Good compendium.......2007-06-26

I am a fan of Dr. Reinhard and enjoyed the book quite a bit. This book surveys a number of the current methods for HDR Imaging and HDR compression. I wish it spent more time looking at the characteristics of the anatomy that we're trying to fool. For one thing, I am singularly unimpressed with Gaussian-based methods that cause halos around objects. The center-surround structure within the retina does not indicate a set of Gaussian (or even close!) weights as distance tails off. We use Gaussians because the math is easy, the function is separable, and largely for historic reasons.

Undoubtedly, as the amount of computer power available continues to increase, and as we make better statistical models for edges and detail, we will be able to make a print that is more closely compatible with the "mental sketch" that we hold in our heads that CAN cover a large dynamic range. This is a good first and second step.

HDR - State of the Art .......2006-01-29

This book is exactly what many people had hoped for, a high level book - that explains all the concepts beyond the basics- which can found elsewhere.
If your not already aware - people like Greg Ward and Paul Debevec invented the area of HDR and its early implementations, and their work and that of their colleagues, continues to be at the very leading edge of research in the area.
This is not a light weight glossy coffee table book - it is a factual, informative book that explains the logic and maths of HDR, while remaining really well written. It will become the default text on the subject for some time, and it is a valuable book for anyone serious about computer graphics and photography/imaging.
I could not recommend it more strongly for serious reader -but not a present for your Mum (unless she works at ILM or Pixar) !

Not for the artist or photographer.......2006-01-28

Finally we have the first book on High Dynamic Range Imaging or "HDRI". With a very general title like this you might be left wondering what is exactly covered within this book, and this review will help to answer that question. It's surprising that this is the first book on HDRI - the technique of shooting HDRIs and using it to achieve photorealistic results has been an indispensable tool in the film and computer graphics industry for years. Recently many software developers have integrated HDRI support into their software making it even easier than before to use this advanced technique. We even have HDRI capable cameras and real-time HDRI appearing in computer games. So for people wishing to break into this field, this book is long overdue.

Please keep in mind that this review is being performed from an artist's perspective, hence I am unable to provide much useful information regarding the more technical aspects of this book, of which there are many! For this I have spoken to one of the authors, Greg Ward, who has provided us with a more detailed insight.

The book is a quality hardcover tome of information containing healthy numbers of full color images, formulas and graphs. It also comes with a DVD full of useful resources, the contents of which are outlined below. While most chapters have a short introductory paragraph that can be understood by the layman or artist, they quickly move into the realm of highly complex formulas and code. If you're expecting this book to have some tutorials on lighting and rendering a HDR image in 3dsmax or Lightwave you're looking at the wrong book. The sections that do cater for the artist are mainly available online anyway, along with numberless websites that offer easy to read, quick and dirty tutorials and how-to's.

The publisher's description of the audience says the book is for anyone who works with images, but if you are specifically a photographer or a computer graphics artist then this book is very light on useful, practical information. If you read a chapter on removing lens flare or movement from your HDRIs it will be a technical explanation containing formulas and code, not a how-to on removing it using your favorite image editor.

Greg Ward has provided us with some more insight into who would find the book most useful, and what level of skill is required to understand and apply the concepts within:
"For the most part, our intended audience includes computer graphics students, teachers, researchers, and professionals, as well as special effects technical directors and game developers who are interested in applying HDR in their work. The book is geared towards computer graphics and vision graduate students and above (including professors, researchers, and professionals). It attempts to cover all of the fundamentals of HDR imaging and delves into some more advanced topics as well, but was not designed as a recipe book or anything of that sort. The reader is left with a fair amount of work to do to apply the concepts presented."

DVD
The book includes a DVD, which contains 4 gigs worth of resources that are easily navigated via a html browser. The contents include:
* HDR Images in various formats (very large number of images)
* Executables and a set of libraries for converting images between Radiance HDR and JPGHDR format developed by Greg Ward at SunnyBrook Tech.
* Source Code and exes for more than 20 tone reproduction operators.
* IBL tutorial using Radiance by Paul Debevec (very simple)

Other Notes
While this book mainly caters for the technically minded, there are several gems such as links for providers of leading edge HDRI capable still and video cameras, and a list of chrome ball manufacturers. HDRI hardware and software is also touched on as well as an interesting chapter on the human visual system.

Conclusion
For the artist or photographer we are still waiting for that first HDRI book, but for the computer scientist or programmer this book is definitely for you. It's hard to beat a book written about HDRI by the pioneers of HDRI.

A great resource.......2005-12-29

This book covers the basic concepts (including just enough about human vision to explain why HDR images are necessary), image capture, image encoding (not as easy as it sounds), file formats, display techniques, tone mapping for lower dynamic range display (FAR from easy), and the use of HDR images and calculations in 3D rendering (which is very cool, even if you aren't working in 3D). The range and depth of coverage is good for the knowledgeable researcher as well as those who are just starting to learn about High Dynamic Range imaging.

I have found this book very useful in my own work. This is a great collection of the existing research on HDR imaging plus quite a bit of previously unpublished work from the authors. I have loaned or recommended the book to several coworkers to introduce them to the concepts behind HDR or help them in their own implementation of HDR imaging. (and so far, they're all liking the book, too)

If you are working with HDR images, think you will be, or wonder what all the fuss is about, you really should read this book.

Connectionist Speech Recognition: A Hybrid Approach (The International Series in Engineering and Computer Science)

Average customer rating:

Connectionist Speech Recognition: A Hybrid Approach (The International Series in Engineering and Computer Science)
Hervé A. Bourlard , and Nelson Morgan
Manufacturer: Springer
ProductGroup: Book
Binding: Hardcover

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ASIN: 0792393961

Book Description

Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state of the art continuous speech recognition systems based on hidden Markov models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e. HMM emission probability estimation and feature extraction. The book describes a successful five-year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical systems. Using standard databases and comparison with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. The book is also suitable as a text for advanced courses on neural networks or speech processing.

Average customer rating:

An excellent book too
excellent tools for implementation of P.R. techniques

Netlab
Ian T. Nabney
Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Pattern Recognition and Machine Learning (Information Science and Statistics)
Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition
Pattern Recognition and Neural Networks

Accessories:

Intermediate Robot Building
Introduction to Evolutionary Computing (Natural Computing Series)
Artificial General Intelligence (Cognitive Technologies)

ASIN: 1852334401

Book Description

This volume provides students, researchers and application developers with the knowledge and tools to get the most out of using neural networks and related data modelling techniques to solve pattern recognition problems. Each chapter covers a group of related pattern recognition techniques and includes a range of examples to show how these techniques can be applied to solve practical problems.

Features of particular interest include:

- A NETLAB toolbox which is freely available
- Worked examples, demonstration programs and over 100 graded exercises
- Cutting edge research made accessible for the first time in a highly usable form
- Comprehensive coverage of visualisation methods, Bayesian techniques for neural networks and Gaussian Processes

Although primarily a textbook for teaching undergraduate and postgraduate courses in pattern recognition and neural networks, this book will also be of interest to practitioners and researchers who can use the toolbox to develop application solutions and new models.

"...provides a unique collection of many of the most important pattern recognition algorithms. With its use of compact and easily modified MATLAB scripts, the book is ideally suited to both teaching and research."
Christopher Bishop, Microsoft Research, Cambridge, UK

"...a welcome addition to the literature on neural networks and how to train and use them to solve many of the statistical problems that occur in data analysis and data mining" Jack Cowan, Mathematics Department, University of Chicago, US

"If you have a pattern recognition problem, you should consider NETLAB; if you use NETLAB you must have this book." Keith Worden, University of Sheffield, UK

Customer Reviews:

An excellent book too.......2005-03-17

This is actually a must-have book for those who want to study pattern recognition.

excellent tools for implementation of P.R. techniques.......2002-06-25

i first bought the book by Bishop (Neural Network for Pattern Recognition) and anyone who have read it can tell u how excellent that book is. This book has a little bit less theory and more on implementation which is perfect for me. This book include all the topics covered in Bishop and then some. How the book is organized, and how concise, easy to understand the material is at the same amazing level as Bishop's. I believe implementing and practicing things u learn is key to understanding them.. if you just look at how things are implemented, things would suddenly become 10 times clearer for you.. often to your own amazement (that you can actually understand all those stuff). this book is extremely useful even if u dont have matlab (just look up the syntax at mathworks web site), cuz matlab code is straightforward to understand. and the material included is very up to date and cutting edge indeed. i highly highly recommend it.

Classification, Clustering and Data Analysis

Average customer rating:

Understand clusters and clustering deeply
different methods for finding clusters

Classification, Clustering and Data Analysis

Manufacturer: Springer
ProductGroup: Book
Binding: Paperback

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Similar Items:

Cluster Analysis
Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), ... Data Analysis, and Knowledge Organization)
Finding Groups in Data: An Introduction to Cluster Analysis (Wiley Series in Probability and Statistics)

ASIN: 354043691X

Book Description

This book deals with recent developments in classification and data analysis and presents new topics which are of central interest to modern statistics. In particular, these include: classification models and clustering methods, multivariate data analysis, symbolic data, neural networks and learning devices, phylogeny and bioinformatics, new software systems for classification and data analysis, as well as applications in social, economic, biological, medical and other sciences. The book presents a long list of useful methods for classification, clustering and data analysis. By combining theoretical aspects with practical problems it is designed for researchers as well as for applied statisticians and will support the fast transfer of new methodological advances to a wide range of applications.

Customer Reviews:

Understand clusters and clustering deeply.......2006-08-19

This is a good and broad approach about cluster and clustering. It is better for those who want to understand deeply the theme. Is has lot of formulas and mathmatics.

different methods for finding clusters.......2005-01-13

The book has a nice treatment of the problem of finding, in some sense, clusters in data. Several papers point out that there is often some subjectivity here, as to which data sits in a particular cluster. Fuzziness in the boundary of a cluster. It can depend on what your underlying model is.

Possibly of interest to some is work on high dimensionality data, and trying to find clusters in these. Even visualisations might be non-trivial.

The book has value in letting you see a variety of ideas for finding clusters. Perhaps some of these might prove germane to your research.

Average customer rating:

not for the faint at heart, but such a pleasure to read
The inner workings of a neural net
advanced and important work
A synthesis, not an introduction
Didn't get anything out of it.

Pattern Recognition and Neural Networks
Brian D. Ripley
Manufacturer: Cambridge University Press
ProductGroup: Book
Binding: Hardcover

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Similar Items:

Neural Networks for Pattern Recognition
Pattern Classification (2nd Edition)
Classification and Regression Trees
The Elements of Statistical Learning
Pattern Recognition and Machine Learning (Information Science and Statistics)

ASIN: 0521460867

Amazon.com

This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. Almost all the results have proofs that are often original. On the application side, the emphasis is on pattern recognition. Most of the examples are from real world problems. In addition to the more common types of networks, the book has chapters on decision trees and belief networks from the machine-learning field. This book is intended for use in graduate courses that teach statistics and engineering. A strong background in statistics is needed to fully appreciate the theoretical developments and proofs. However, undergraduate-level linear algebra, calculus, and probability knowledge is sufficient to follow the book.

Book Description

Ripley brings together two crucial ideas in pattern recognition: statistical methods and machine learning via neural networks. He brings unifying principles to the fore, and reviews the state of the subject. Ripley also includes many examples to illustrate real problems in pattern recognition and how to overcome them.

Customer Reviews:

not for the faint at heart, but such a pleasure to read.......2004-03-03

Let me start by saying that this book assumes a lot of background, especially in statistics. It dives into the math right away without even a hint or a gentle slope. But what I appreciate is that math is never used for its own sake, it is always justified. The book starts with the introduction to the problems neural nets are to be applied to - pattern recognition task. It proceeds to the elements of statistical decision theory, then goes up to linear discriminant analysis and perceptrons, then up you go to feed-forward neural nets. Non-parametric models and tree-based classifiers are covered next. Belief networks and unsupervised methods (MDS, clustering, etc..) follow. Coverage is extensive, although I would like to see more in the areas of unsupervised learning, which is quite foundational to the whole business.

What sells me on this book quite frankly is that is always keeps an eye on a real-world example. No model or algorithm is introduced without a real-world problem it was intended to solve. You would be better served by the Bishop book (Neural Networks for Pattern Recognition, by C.Bishop ISBN:0198538642) if you are looking for a quick introduction. I would say Ripley's book is the perfect second book on the subject.

I must aplaud the editors and designers of the book. A book itself, apart from the material it covers, is an aestetically most pleasent creation for the somewhat dry subject. Its use of margins is a piece of art - margins are wide, accessible, important points are highlighted there, and you can get to the needed point by flipping the pages quickly. The quality of paper is very good, the book opens wells, and holds its form very well. If you take it seriously and use it often, these qualities will gain in importance.

The inner workings of a neural net.......2004-02-05

I concur with the other reviewers. This book requires the reader to be proficient in many different disciplines. It is extremely difficult to digest if you lack an in-depth background in statistics (Bayes theory etc.), calculus and advanced algebra. Many sections of this book were used as a part of Ripley's graduate courses taught at Cambridge where is still a professor of applied statistics. Where I part company with many of the reviewers is that I will not penalize this book for going over my head at times. It is intended for graduate students in statistics or computer science.

The neural network section explains the workings of NNs that are typically hidden to users of NNs in software packages. In some cases a click of a button is all that is needed to do what is explained in considerable depth in this tome. It can be very useful to fully understand what it is that has happened when a program switch is altered. This prevents using a NN and receiving a naive result that makes unfounded predictions.

advanced and important work.......2001-06-11

If you want a nice up-to-date treatment on neural networks and statistical pattern recognition with lots of nice pictures and an elementary treatment, I recommend the new edition of Duda and Hart. However, neural networks were basically started by the computer-science / artificial intelligence community using analogies to the human nervous system and the perceived connections to the human thought processes. These connections and arguments are weak.

However, a statistical theory of nonlinear classification algorithms shows that these methods have nice properties and have mathematical justification. The statistical pattern recognition research is well over 30 years old and is very well established. So these connections are important for putting neural networks on firm ground and providing greater acceptability from the statistical as well as the engineering community.

Ripley provides a theoretical threatment of the state-of-the-art in statistical pattern recognition. His treatment is thorough, covering all the important developments. He provides a large bibliography and a nice glossary of terms in the back of the book.

Recent papers on neural networks and data mining are often quick to generate results but not very good at providing useful validation techniques that show that perceived performance is not just an artifact of overfitting a model. This is an area where statisticians play a very important role, as they are keenly aware through their experience with regression modeling and prediction, of the crucial need for cross-validation. Ripley covers this quite clearly in Section 2.6 titled "How complex a model do we need?"

It is nice to see the thoroughness of this work. For example, in error rate estimation, many know of the advances of Lachenbruch and Mickey on error rate estimation in discriminant analysis and the further advances of Efron and others with the bootstrap. But in between there was also significant progress by Glick on smooth estimators. This work has been overlooked by many statisticians probably because some of it appears in the engineering literature (but one important paper was in the Journal of the American Statistical Association [JASA] in 1972). To some extent this oversight may be due to the fact that it was not mentioned in Efron's famous 1983 JASA paper and hence is usually missed in the bootstrap literature. Bootstrap methods and cross-validation play a prominent role in this text.

This is an excellent reference book for anyone seriously interested in pattern recognition research. For applied and theoretical statisticians who want a good account of the theory behind neural networks it is a must.

A synthesis, not an introduction.......2000-09-29

This text is wonderful. As some have pointed out, it might not be the best introduction to statistical pattern recognition and classification. Not every text should strive to be introductory, however, and this work shines for other reasons. The true strength of the book is its synthesis of material from diverse domains in a single text. My experience has been in the realm of statistics, and it was insightful to find that neural network approaches share much with traditional classification and discrimination techniques. I find the book enlighting not so much because it explains a given technique in great detail, but because it explains how a number of techniques are related and differ from one another. In this sense, it has opened up a whole new world of approaches to problems I encounter, that I had previously deemed inapplicable because they were "AI engineering techniques" or some such thing. If you want to learn about the details of a particular approach to pattern recognition--e.g., ICA, kohonen maps, SVM, etc.--find a different text. If you want an overview of the field of pattern recognition, however, buy this text. It provides a comprehensive, integrative perspective on classical and modern techniques from a number of disciplines. In fact, I would recommend this text as a complement to a more detailed text on a given pattern recognition technique--the one will fill in the details Ripley necessarily skims, and Ripley will explain how the technique is related to everything else.

Didn't get anything out of it........2000-08-07

After sitting down several times and attempting to learn something from Ripley's Pattern Recognition book I am frustrated each time. I wish Ripley could be a better author. From his writings you can see he knows a lot about Neural Networks, but cannot relate it to the reader. The text is VERY heavy in mathematical formulas (about 1/3 page of pure math per page). Another third of the book are references to other papers (There are 35 pages of references. Ripley cites about 1000 different papers.). That doesn't leave a lot left over for the reader. I suggest this book only for those already familiar enough with Neural Nets to have their papers cited by Ripley.

Book Description

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

Download Description

"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."

Customer Reviews:

The most comprehensive book about machine learning.......2007-02-28

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.

Very Bad treatment of the subject.......2006-01-12

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.

This book is good guidance........2000-11-03

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!!

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