[.ca] Pattern Classification (ISBN 0471056693)

Still one of the better books nowadays....:
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.

Pretty Bad:
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."

Good Book for Pattern Recognition:
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.

Excellent Introductory Text and Reference Tool:
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.

Definitely The Best:
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 .