Rating: 
Summary: Pattern Classification by Duda et al.--2nd Edition
Review: 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.
Rating: 
Summary: Pure Disappointment !
Review: The book is the opposite of what I have expected. Unlike to the first volume by Duda and Hart, this book does not go to the roots on many subjects, the review is not comprehensive, but poor and biased, most important keywork, including that which has been greatly reviewed in the first edition, is not there, crucial links between methods and key concepts are not fully understood. References are incomplete and ignore the true key work of so many authors - what a shame ! The first edition by Duda and Hart is one of the best books ever written on the subject. It makes me feel very much sad to compare the two books. The second edition should have never been written.
Rating: 
Summary: Review of Duda, Hart and Stork's "Pattern Recognition"
Review: The original book by Duda and Hart has been the bible of the machine learning community. In recent years there has been a spate of books aspiring to that mantle. For the most part, these have suffered from being too close to the subject of statistical inference in its current incarnation. One suspects they will quickly grow dated, as the current machine learning fads-du-jour pass. In contrast, the revised version of the book, ably sheparded by David Stork, stays true to the vision of the original. It elaborates the fundamental issues underlying machine learning in a clear manner, with a keen eye for the broad perspective. The reader should be aware though that in order to make the text extremely accessible, some of it is misleadingly simplified, and some crucial references are left out. The book is clearly written, and should be understandable by any technically trained undergraduate or graduate student. All in all, a joy to have on one's shelf.
Rating:
Summary: Full of mistakes causing much frustration
Review: This book causes much frustration. Simple concepts are made hard to understand. Hard concepts are hidden behind incorrect formulas. There are so many mistakes in the book that by the end you will have no faith in any formula set forth by the authors.
Rating: 
Summary: Disappointing
Review: This book is a revised edition of Duda and Hart's classic text "Pattern Recognition and Scene Analysis" which was originally published in 1973. In fact, the 1973 edition of the book played a pivotal role in introducing me (and countless researchers of my generation) to the field of pattern recognition. Needless to say, I was looking forward to the release of the revised edition. Unfortunately, I was extremely disappointed with the new edition. I had expected much more from the masters: Duda and Hart! My reasons for disappointment with this book are as follows: Given the 27 years that have elapsed since the publication of the first edition of the book, and the immense progress that has taken place in pattern recognition, machine learning, computational learning theory, grammar inference, statistical inference, algorithmic information theory, and related areas, the revisions and additions in the 2000 edition are essentially of a patchwork nature. In my opinion, they do not reflect the current understanding of the topic of pattern recognition. A disproportionate number of pages are devoted to topics like density estimation despite the fact that it has been well established in recent years, through the work of Vapnik and others, that when working with limited data, trying to solve the problem of pattern classification through density estimation (which turns out to be, in a well-defined sense of the term, a much harder problem than pattern classification) is rather futile. When modern techniques for learning pattern classifiers from limited data sets (e.g., support vector classifiers) are touched on in the book, the treatment is disappointingly superficial and in some cases, misleading. There is virtually no discussion of problems of learning from large high dimensional data sets, incremental refinement of classifiers, learning from sequential data, distributed algorithms, etc. The treatment of non-numeric pattern recognition techniques (e.g., automata, languages, etc.) is extremely superficial. There is almost no discussion of essential aspects such as preprocessing and feature extraction techniques for dealing with variable length, semistructured, or unstructured patterns. There is very little contact made with a large body of pattern recognition algorithms, results, and approaches developed by the machine learning community, with the possible exception of the decision tree algorithm. There is little discussion of the extremely important topic of computational complexity and data requirements of learning algorithms. On the positive side, the discussion of most topics that were originally covered in the 1973 edition has been further refined and in many cases, made more accessible through the addition of illustrative examples and diagrams. Topics such as Bayesian networks receive an intutive and accessible treatment. The exercises at the end of each chapter seem useful Perhaps it is too difficult for any individual or a small group of individuals to write a textbook that reflects the state of the art in pattern recognition. Perhaps my expectations of Duda and Hart (based largely on the extraordinary job that did on the 1973 edition of their book) were too high to have a reasonable chance of being met by the 2000 edition. Perhaps I have come to expect more out of graduate level textbooks after having worked as a researcher and an educator in this field for over a decade at a major university. In short, the book fell significantly short of my expectation.
Rating:
Summary: Disappointing
Review: This book is a revised edition of Duda and Hart's classic text "Pattern Recognition and Scene Analysis" which was originally published in 1973. In fact, the 1973 edition of the book played a pivotal role in introducing me (and countless researchers of my generation) to the field of pattern recognition. Needless to say, I was looking forward to the release of the revised edition. Unfortunately, I was extremely disappointed with the new edition. I had expected much more from the masters: Duda and Hart! My reasons for disappointment with this book are as follows: Given the 27 years that have elapsed since the publication of the first edition of the book, and the immense progress that has taken place in pattern recognition, machine learning, computational learning theory, grammar inference, statistical inference, algorithmic information theory, and related areas, the revisions and additions in the 2000 edition are essentially of a patchwork nature. In my opinion, they do not reflect the current understanding of the topic of pattern recognition. A disproportionate number of pages are devoted to topics like density estimation despite the fact that it has been well established in recent years, through the work of Vapnik and others, that when working with limited data, trying to solve the problem of pattern classification through density estimation (which turns out to be, in a well-defined sense of the term, a much harder problem than pattern classification) is rather futile. When modern techniques for learning pattern classifiers from limited data sets (e.g., support vector classifiers) are touched on in the book, the treatment is disappointingly superficial and in some cases, misleading. There is virtually no discussion of problems of learning from large high dimensional data sets, incremental refinement of classifiers, learning from sequential data, distributed algorithms, etc. The treatment of non-numeric pattern recognition techniques (e.g., automata, languages, etc.) is extremely superficial. There is almost no discussion of essential aspects such as preprocessing and feature extraction techniques for dealing with variable length, semistructured, or unstructured patterns. There is very little contact made with a large body of pattern recognition algorithms, results, and approaches developed by the machine learning community, with the possible exception of the decision tree algorithm. There is little discussion of the extremely important topic of computational complexity and data requirements of learning algorithms. On the positive side, the discussion of most topics that were originally covered in the 1973 edition has been further refined and in many cases, made more accessible through the addition of illustrative examples and diagrams. Topics such as Bayesian networks receive an intutive and accessible treatment. The exercises at the end of each chapter seem useful Perhaps it is too difficult for any individual or a small group of individuals to write a textbook that reflects the state of the art in pattern recognition. Perhaps my expectations of Duda and Hart (based largely on the extraordinary job that did on the 1973 edition of their book) were too high to have a reasonable chance of being met by the 2000 edition. Perhaps I have come to expect more out of graduate level textbooks after having worked as a researcher and an educator in this field for over a decade at a major university. In short, the book fell significantly short of my expectation.
Rating:
Summary: Disappointing
Review: This book is a revised edition of Duda and Hart's classic text on Pattern Classification which was originally published in 1973. In fact, the 1973 edition of the book played a pivotal role in introducing me (and countless researchers of my generation) to the field of pattern classification. Needless to say, I was looking forward to the release of the revised edition. Unfortunately, I was extremely disappointed with the new edition. I had expected much more from the masters: Duda and Hart! My reasons for disappointment with this book are as follows: Given the 27 years that have elapsed since the publication of the first edition of the book, and the immense progress that has taken place in pattern recognition, machine learning, computational learning theory, grammar inference, statistical inference, algorithmic information theory, and related areas, the revisions and additions in the 2000 edition are essentially of a patchwork nature. In my opinion, they do not reflect the current understanding of the topic of pattern classification. A disproportionate number of pages are devoted to topics like density estimation despite the fact that it has been well established in recent years, through the work of Vapnik and others, that when working with limited data, trying to solve the problem of pattern classification through density estimation (which turns out to be, in a well-defined sense of the term, a much harder problem than pattern classification) is rather futile. When modern techniques for learning pattern classifiers from limited data sets (e.g., support vector classifiers) are touched on in the book, the treatment is disappointingly superficial and in some cases, misleading. There is virtually no discussion of problems of learning from large high dimensional data sets, incremental refinement of classifiers, learning from sequential data, distributed algorithms, etc. The treatment of non-numeric pattern recognition techniques (e.g., automata, languages, etc.) is extremely superficial. There is almost no discussion of essential aspects such as preprocessing and feature extraction techniques for dealing with variable length, semistructured, or unstructured patterns. There is very little contact made with a large body of pattern classification algorithms, results, and approaches developed by the machine learning community, some exceptions. There is little discussion of the extremely important topic of computational complexity and data requirements of learning algorithms. On the positive side, the discussion of most topics that were originally covered in the 1973 edition has been further refined and in many cases, made more accessible through the addition of illustrative examples and diagrams. Topics such as Bayesian networks receive an intutive and accessible treatment. It was good to see a treatment of techniques for combining classifiers (although it is placed misleadingly in a chapter titled "Algorithm-Independent Machine Learning" which has an organization that is reminescent of a "kitchen sink"). The exercises at the end of each chapter seems useful. Perhaps it is too difficult for any individual or a small group of individuals to write a textbook that reflects the state of the art in pattern recognition. Perhaps my expectations of Duda and Hart (based largely on the extraordinary job that did on the 1973 edition of their book) were too high to have a reasonable chance of being met by the 2000 edition. Perhaps I have come to expect more out of graduate level textbooks after having worked as a researcher and an educator in this field for over a decade at a major university. In short, the book fell significantly short of my expectation.
Rating:
Summary: Good Book for Pattern Recognition
Review: This book is full of useful algorithms, as well as the theory behind them. The explanations are good, although they sometimes require reading them several times to fully grip what is going on and why it works (but this is the case with many useful algorithms). This is a must-have for pattern rec work.
Rating:
Summary: Still one of the better books nowadays....
Review: This book is not for the novice, and it assumes some mathematical skills on the reader's side. Having read the book a few times now, I must conclude that this one covers a lot of ground regarding pattern classification, and is probably more complete than any other book currently on the market. If you're really interested in pattern recognition, you will get through this book with success, and will feel very thankful about the many useful algorithms, all perfectly clarified with pictures and even pseudo code. For those having mathematical problems, you might have to read more than once or twice to get a good grip on it. Yes, there are a few bugs in there, but this is the same with anyother book (there is an errata available on the web). I've also been implementing many of the algorithms discussed, and I think anybody seriously involved with pattern classification should have at least a copy of this book nearby. For those who complain that the book doesn't cover enough topics, related to distributed processing, machine learning, statistical inference, etc, I think these topics don't belong in here (they deal less with pattern classification), and others have dedicated separate books for all of those topics. For those readers/students complaining about "too complicated", or "too many errors", or "hard-to-understand concepts", I recommand a better science teacher. I haven't read the first edition, but having been in the commercial field of data mining / data fore casting / data clustering for many years now, I think this book is very up-to-date. I have come to understand what works and what doesn't, and yes, maybe not everything is covered, but the things that are covered are definitely current and leading-edge technology.
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