Pattern Classification (2nd Edition) (Hardcover)

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Editorial Reviews

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)

"...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles." -- Zentralblatt MATH, Vol. 968, 2001/18

"...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

"...strongly recommended both as a professional reference and as a text for students..." -- Technometrics, February 2002

"This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." -- Mathematical Reviews, Issue 2001k

"attractively presented and readable" -- Journal of Classification, Vol.18, No.2 2001

Product 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.

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Product Details

• Hardcover: 654 pages
• Publisher: Wiley-Interscience; 2 edition (October 2000)
• Language: English
• ISBN-10: 0471056693
• ISBN-13: 978-0471056690
• Product Dimensions: 10.2 x 7.2 x 1.3 inches
• Shipping Weight: 2.9 pounds (View shipping rates and policies)
• Average Customer Review: 3.7 out of 5 stars  See all reviews (29 customer reviews)
• Amazon.com Sales Rank: #105,515 in Books (See Bestsellers in Books)

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Customer Reviews

Most Helpful Customer Reviews

70 of 73 people found the following review helpful:

5.0 out of 5 stars Pattern Classification by Duda et al.--2nd Edition, December 28, 2000

By	Lyndon S Hibbard (St. Louis, MO USA) - See all my reviews

The 1973 edition of Pattern Classification by Richard Duda and Peter Hart is one of the most cited books in the fields of image processing, machine vision, and classification. It contains perhaps the clearest, most comprehensible descriptions of statistical inference ever written. Though intended for the image processing audience, it is general in its approach, and is broader in coverage than other contemporary books like the redoubtable Van Trees (1969). The section on Bayesian Learning anticipates the EM algorithm which appeared a few years later (Dempster, et al. 1977) and their description of Parzen windows for density estimation is more often cited than Parzen's own papers.

The appearance of the 2000 2nd edition led this writer to wonder if D&H could repeat with an offering as good as their first. In particular, would D&H have kept up with the considerable growth in methodology in the 1990s? Well, they have! With the addition of David Stork as third author, the second addition re-presents the basic theory, illustrated with some beautiful and complex figures, and knits it neatly with an exposition of neural networks, stochastic methods for posterior determination, nonmetric classification (tree search and string parsing), and clustering. Chapter 9 is a particularly interesting review of the recent machine learning research making the point that, absent knowledge of a problem's specific domain, no one classifier is better that any other. This chapter also reviews solutions to the problem of training on too-small samples including the Jackknife and bootstrap methods, and newer bagging and boosting algorithms popular in data mining applications. Each chapter is well-designed, with a summary, many exercises (including computer exercises), and references to the literature (typically 50-100) including many recent references.

This book is designed for an upper-level undergraduate/graduate audience. It doesn't assume a knowledge of statistics, but requires some familiarity with methods from calculus, real analysis, and linear algebra.

The first edition was a particularly important element in this writer's education; the second edition is certain to find a similar place in the working and intellectual lives of many new readers.

29 of 29 people found the following review helpful:

5.0 out of 5 stars excellent revision of a classical text on statistical pattern recognition , January 23, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

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.

26 of 26 people found the following review helpful:

5.0 out of 5 stars Introducing the New Heavy Weight Champion, April 25, 2001

By	Todd Ebert (Long Beach California) - See all my reviews

Before this book was published, I considered "Pattern Recognition", by Theordoridis to be the best text for learning pattern recognition and classification. Although Theordoridis' book has some difficulties (not enough concrete exercises, ommission of structural methods, and not enough material on Bayesian Networks and HMMs), it seemed significantly better than previous texts. However, not only does Duda, Hart, and Stork's book succeed in those areas where the former fails, but it also has other strengths that the former book does not have: better illustrations, boxed formulas and algorithms, and highlighted defintions. Although somewhat superficial, these improvements mark the fact that pattern recognition is now considered a mainstream subject, and thus requires a mainstream text that keeps the integrity and rigor of the subject matter, while simultaneously making it more accessible to the average engineer. The new champ, however, does not come without it's own shortcomings. For example, I believe the last 3 chapters of Theodoridis' book should be read by anyone who wants a deeper understanding of clustering techniques for unsupervised learning. Moreover, this book fails to acknowledge the brilliant work done in computational learning by Vapnik and Chervonenkis, which reveals the authors' bias towards practice over theory. I believe it deserves more than passing mention in the historical notes section of unsupervised learning.