Rating: 
Summary: Introducing the New Heavy Weight Champion
Review: 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:
Summary: Introducing the New Heavy Weight Champion
Review: 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:
Summary: Definitely The Best
Review: Everything You ever want to know about the subject for intermediate .
Handbook of industries methods and algorithms for advanced .
Together with Mitchell perfect for beginners .
Rating: 
Summary: Pretty Bad
Review: I am using this book for class right now. Our professor complains about the book constantly because 1) the text is explained in too complicated of a way, 2) there are too many errors, and 3) some of the errors are quite mathematical in nature. Our professor said he tried to E-mail the author, but the author said he "didn't have time because so many people like the book."
Rating:
Summary: Excellent reference book
Review: I found book very useful. Figures, mathematical explanations and algortihms provides complemantry information to understand topic better. There may some errors in the book but I did not found any fatal one. Problem questions of each chapter are very useful. This is a must book whom are interesting in pattern classification area both in industry and academy.
Rating: 
Summary: Pattern Classification
Review: I found this book quite useful as an augmentive text to Elements of Statistical Learning used in a grad engineering level data mining course. This book is written more at an engineering level, and I found it to bridge well between advanced texts such as Elements of Statistical Learning and more general audience books that really are lacking. Duda and Hart do a good job at explaining the concepts, however some techniques only recieve a cursory overview while other topics are rather elaborated upon, however this may have been done by the authors experience of which techniques are commonly employed in practice. The excercises at the end of the chapters include a lot of hands on programming and computer-based assignments which I found useful, and a MATLAB workbook associated with this is also offered, however I have not read this book. Nonetheless I have implemented some of the concepts in this book using Matlab and it definately does help to cement the idea, even if this is just serves as an intellectual excercise and isn't intended to be used for anything else. With a little bit of digging through the help or using a book such as Ripley and Venerable's Modern Applied Statistics with S, most if not all of the techniques can be explored using the R statistical software.
Rating:
Summary: Definite Page-turner!
Review: I found this book to be an absoluting intriguing look into the magnificent world of pattern classification. Duda leads us into this oft misunderstood labrynth of confusion with a delicate guide to the underlying mathematical formulae. From this gentle introduction, we are given a firm platform from which to spring ourselves into the delightful playground of neural, bayesian and regressive classification systems. Markovian processes, sigmoidal thresholds, these are your captors as you blissfully move from page to page.Duda has a unique ability to blend mathematics and prose into a body of work worthy of the highest praise. This will undoubtedly be a most difficult year for the Nobel commission as they are tormented over deciding whether Duda's 'Pattern Classification' belongs in the literature or mathematics category. Suffice it to say that this book has found a permanent, loving home in my bookcase between 'War and Peace' and 'Advanced Differential Equations, 2nd edition'.
Rating:
Summary: Excellent Introductory Text and Reference Tool
Review: 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.
Rating:
Summary: Excellent Introductory Text and Reference Tool
Review: 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.
Rating:
Summary: not exactly a revision
Review: 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.
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