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Title: Pattern Classification (2nd Edition) by Richard O. Duda, Peter E. Hart, David G. Stork ISBN: 0-471-05669-3 Publisher: Wiley-Interscience Pub. Date: October, 2000 Format: Hardcover Volumes: 1 List Price(USD): $125.00 |
Average Customer Rating: 3.69 (13 reviews)
Rating: 4
Summary: not exactly a revision
Comment: 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.
Rating: 5
Summary: Introducing the New Heavy Weight Champion
Comment: 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.
Rating: 4
Summary: Excellent Introductory Text and Reference Tool
Comment: If you think that some method such as SVM is the "holy grail" of machine learning and pattern recognition and are interested only in an in-depth coverage of that specific tool, this book is not for you. If, however, you want to understand the basic concepts and methods employed by a broad range of researchers and scientists, I highly recommend buying it.
The book covers a broad range of topics in pattern recognition. Its explanations are lucid, and its illustrations are helpful. The book is well-written and well-organized. When using this book as part of a low-level graduate course, I was not particularly impressed. Recently, however, I have found myself frequently going back to the book to refresh my understanding of the basic idea of some topic. I recommend PC as a companion text for a course in pattern recognition. I also recommend purchasing the book for private use.
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Title: The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman ISBN: 0387952845 Publisher: Springer Verlag Pub. Date: 09 August, 2001 List Price(USD): $82.95 |
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Title: Neural Networks for Pattern Recognition by Christopher M. Bishop, Chris Bishop ISBN: 0198538642 Publisher: Oxford University Press Pub. Date: January, 1996 List Price(USD): $65.00 |
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Title: Computer Manual in MATLAB to Accompany Pattern Classification, Second Edition by David G. Stork, Elad Yom-Tov ISBN: 0471429775 Publisher: Wiley-Interscience Pub. Date: 02 April, 2004 List Price(USD): $34.95 |
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Title: Statistical Pattern Recognition, 2nd Edition by Andrew R. Webb ISBN: 0470845147 Publisher: John Wiley & Sons Pub. Date: 15 October, 2002 List Price(USD): $59.95 |
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Title: An Introduction to Support Vector Machines and Other Kernel-based Learning Methods by Nello Cristianini, John Shawe-Taylor ISBN: 0521780195 Publisher: Cambridge University Press Pub. Date: 23 March, 2000 List Price(USD): $53.00 |
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