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In just a few easy steps below, you can become an online reviewer. You'll be able to make changes before you submit your review. Extensive errata From Amazon The errata for this book is so extensive that it makes it unreadable.

Better wait for the third edition. Topic covered seriously From Amazon My impression reading the book is that it was very carefully written. Don't speak too broad and too general but also include the fundamental topics with some examples. Such topics might be old and even unused but they form the understanding and create solid basis for further studies in this field. The minor drawback i didn't read and compared everything to old book but it seems that this book is simply rewrite or "correction of mistakes" if comparing previous edition.

Well deserved reputation as a classic! From Amazon What a tough area to publish in! The mathematics underlying many of these techniques are extremely advanced, often out of the reach of the target audience. The end goal of a book such as this one is to give the reader an understanding of the range of techniques available, the advantage and dissadvantage of each, and a sense of when each is appropriate. Duda and Hart find the right balance.

The math is rigorous, not ponderous. I found their figures to be some of the most powerful representations of multivariate manifold concepts I have seen anywhere. They are my 'go-to' for an explanation of Fisher Discriminant Analysis, a common technique covered by many others. The only drawback is really one of the field as a whole.

Pattern recognition is a collection of techniques with a common application, and lacks the underlying unification of more fundamental disciplines. Terrible Problems From Amazon I am not sure how this book gets consistently high marks. I am using this text for a graduate level course.

While it does a decent job covering most of the topics, it has some glaring flaws. For one the Homework Problems it provides are not really representative of what you're learning in the text. Almost all of the problems revolve around proofs, as opposed to using the concepts in practice. You can seemingly have a good grasp on the material, yet spend hours trying to solve each of the problems they provide for that particular section. My entire class has complained, and even my professor has admitted that even he isn't sure sometimes how they expect you to solve some of the problems.

Secondly, there are very few example problems demonstrated in the text, so the reader doesn't really get to see the concepts in action so to speak. Also, there is a typo or error on almost every other page, sometimes even on important formulas.

Overall, I'd have to think there are better books out there. If this truly is "the best there is" as some reviewers claim, God help the field of Pattern Recognition. 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. Chapter 6 covers multilayer neural networks a totally new area. Each chapter has a large number of relevant references and many homework exercises and computer exercises. 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. 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.

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. We use cookies to offer you a better browsing experience. Make sure to accept our cookies in order to get the best experience out of this website. If you would like to read more about this check out the Privacy Policy page.

Education AUB. Important Message. Pattern classification, second edition: 1 a wiley-interscience publication by Richard O. Duda, Peter E. Hart, David G. I Add to my wishlist. Overview Readers reviews 5 Product Details. Your rating Click on the stars for rating. Your review Headline characters remaining Review characters remaining. Add to shopping bag. War and the City: Urban Geopolitics Copyright - Antoine Online - All rights reserved.

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ISBN 13: 9780471056690

The first edition, published in , 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. From the reviews of the First Edition. Stork has done a superb job of updating the book.


Pattern Classification, 2nd Edition




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