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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.
- Sales Rank: #118538 in Books
- Brand: Brand: Wiley
- Published on: 2000-11-09
- Original language: English
- Number of items: 1
- Dimensions: 10.30" h x 1.18" w x 7.30" l, 2.75 pounds
- Binding: Hardcover
- 680 pages
- Used Book in Good Condition
Review
"…it provides a good introduction to the subject of Pattern Classification." (Journal of Classification, September 2007)
"…a fantastic book! The presentation...could not be better, and I recommend that future authors consider…this book as a role model." (Journal of Statistical Computation and Simulation, March 2006)
"...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002)
"...provides information needed to choose the most appropriate of the many available technique for a given class of problems." (SciTech Book News, Vol. 25, No. 2, June 2001)
"I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover!" (Pattern Analysis & Applications Journal, 2001)
"This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." (Mathematical Reviews, Issue 2001k)
"...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles." (Zentralblatt MATH, Vol. 968, 2001/18)
"attractively presented and readable" (Journal of Classification, Vol.18, No.2 2001)
From the Back Cover
From the reviews . . .
"The first edition of this book, published 30 years ago by Duda and Hart, has been a defining book for the field of Pattern Recognition. Stork has done a superb job of updating the book. He has undertaken a monumental task of sifting through 30 years of material in a rapidly growing field and presented another snapshot of the field, determining what will be of importance for the next 30 years and incorporating it into this second edition. The style is easy to read as in the original book and the statistical, mathematical material comes alive with many new illustrations. The end result is harmonious, leading the reader through many new topics..."
–Sargur N. Srihari, PhD, Director, Center for Excellence in Document Analysis and Recognition, Distinguished Professor, Department of Computer Science and Engineering, SUNY at Buffalo
Practitioners developing or investigating pattern recognition systems in such diverse application areas as speech recognition, optical character recognition, image processing, or signal analysis, often face the difficult task of having to decide among a bewildering array of available techniques. This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years. Special features include:
- Clear explanations of both classical and new methods, including neural networks, stochastic methods, genetic algorithms, and theory of learning
- Over 350 high-quality, two-color illustrations highlighting various concepts
- Numerous worked examples
- Pseudocode for pattern recognition algorithms
- Expanded problems, keyed specifically to the text
- Complete exercises, linked to the text
- Algorithms to explain specific pattern-recognition and learning techniques
- Historical remarks and important references at the end of chapters
- Appendices covering the necessary mathematical background
NOTE: Computer Manual in MATLAB to Accompany Pattern Classification, 2e users access toolbox via ftp://ftp.wiley.com/public/sci_tech_med/pattern_classification/ (Note: Visitors will require a password from the Manual to access.)
About the Author
RICHARD O. DUDA, PhD, is Professor in the Electrical Engineering Department at San Jose State University, San Jose, California.
PETER E. HART, PhD, is Chief Executive Officer and President of Ricoh Innovations, Inc. in Menlo Park, California.
DAVID G. STORK, PhD, is Chief Scientist, also at Ricoh Innovations, Inc.
Most helpful customer reviews
35 of 35 people found the following review helpful.
excellent revision of a classical text on statistical pattern recognition
By Michael R. Chernick
The 1973 book by Duda and Hart was a classic. It surveyed the literature on pattern classification and scene analysis and provided the practitioner with wonderful insight and exposition of the subject. In the intervening 28 years the field has exploded and there has been an enormous increase in technical approaches and applications.
With this in mind the authors and their new coauthor David Stork go about the task of providing a revision. True to the goals of the original the authors undertake to describe pattern recognition under a variety of topics and with several available methods to cover each topic. Important new areas are covered and old but now deemed less significant are dropped. Advances in statistical computing and computing in general also dictate the topics. So although the authors are the same and the title is almost the same (note that scene analysis is dropped from the title) it is more like an entirely new book on the subject rthan a revision of the old. For a revision, I would expect to see mostly the same chapters with the same titles and only a few new chapters along with expansion of old chapters.
Although I view this as a new book, that is not necessarily bad. In fact it may be viewed as a strength of the book. It maintains the style and clarity of the original that we all loved but represents the state-of-the-art in pattern recognition at the beginning of the 21st Century.
The original had some very nice pictures. I liked some of them so much that I used them with permission in the section on classification error rate estimation in my bootstrap book. This edition goes much further with beautiful graphics including many nice three-dimensional color pictures like the one on the cover page.
The standard classical material is covered in the first five chapters with new material included (e.g. the EM algorithm and hidden markov models in Chapter 3). Chapter 6 covers multilayer neural networks (a totally new area). Nonmetric methods including decision trees and the CART methodology are covered in Chapter 8. Each chapter has a large number of relevant references and many homework exercises and computer exercises.
Chapter 9 is "Algorithm-Independent Machine Learning" and it includes the wonderful "No Free Lunch" theorem (Theorem 9.1), a discussion of the minimum desciption length principle, overfitting issues and Occam's razor, bias - variance tradeoffs,resampling method for estimation and classifier evaluation, and ideas about combining classifiers.
Chapter 10 is on unsurpervised learning and clustering. In addition to the traditional techniques covered in the first edition the authors include the many advances in mixture models.
I was particularly interested in that part of Chapter 9. There is good coverage of the topics and they provide a number of good references. However, I was a bit disappointed with the cursory treatment of bootstrap estimation of classification accuracy (section 9.6.3 on pages 485 - 486). I particularly disagree with the simplistic statement "In practice, the high computational complexity of bootstrap estimation of classifier accuracy is rarely worth possible improvements in that estimate (Section 9.5.1)". On the other hand, the book is one of the first to cover the newer and also promising resampling approaches called "Bagging" and "Boosting" that these authors seem to favor.
Davison and Hinkley's bootstrap text is mentioned for its practical applications and guidance for bootstrapping. The authors overlook Shao and Tu which offers more in the way of guidance. Also my book provides some guidance for error rate estimation but is overlooked.
My book also illustrate the limitations of the bootstrap. Phil Good's book provides guidance and is mentioned by the authors. But his book is very superficial and overgeneralized with respect to guiding practitioners. For these reasons I held back my enthusiasm and only gave this text four stars.
0 of 0 people found the following review helpful.
Great quality!
By Amazon Customer
The book seems to be bearly used at all. Great quality. Arrived on time as well. No complaints at all
29 of 34 people found the following review helpful.
A Very Bad Sequel
By Book Runner
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.
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