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Pattern Classification (2nd Edition)
enlarge	Author: David G Stork Publisher: Wiley This item is no longer available Rating: 29 reviews Format: Kindle Book Media: Kindle Edition Edition: 2 Pages: 680 Number Of Items: 1 Dewey Decimal Number: 006.4 ASIN: B002LA09W6 Publication Date: October 1, 2000


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Editorial Reviews: 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.

Customer Reviews: Showing reviews 1-5 of 29 1 2 3 4 5 6 Next » summation July 3, 2010 wilkidan Seems a very good book. Could not tell what the little criticism was about. The author goes out of his way to explain and make it pertinet. Happy reading. Extensive errata November 8, 2009 M. Abramson (Arlington, VA USA) 1 out of 1 found this review helpful The errata for this book is so extensive that it makes it unreadable. Better wait for the third edition. Topic covered seriously May 28, 2009 Vladislavs Dovgalecs (Bordeaux, France) 1 out of 2 found this review helpful 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! April 1, 2009 Craig Garvin (Cambridge, MA USA) 2 out of 3 found this review helpful 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. Some authors make such an effort to convey understanding that the book becomes a (rather bad) linear algebra / functional analysis text, while others skip mathematical rigor and present an algorithm laundry list, along with an overly qualitative assessment of strengths & weakness. 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 April 9, 2008 gtdsox (Bristol, RI United States) 7 out of 9 found this review helpful 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. Showing reviews 1-5 of 29 1 2 3 4 5 6 Next »

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