Pattern Recognition 4th Edition

The book "Pattern Recognition" of Theodoridis and Koutroumbas is an excellent one.It covers the field thoroughly, and the material is presented very clearly, bothfrom the mathematical and the algorithm point of view.It includes superb examples andcomputer experiments with which the reader can gain insight to the topics.Also, it is updated with a lot of recent advances on the Pattern Recognition domain,as e.g. Semi-supervised learning, combining classifiers, spectral clustering,nonlinear dimensionality reduction. The presentation of all these advanced material isvery well organized and the reader can follow and understand thesesophisticated mathematically concepts.It is one of my three best books on the topic,the other ones are the "Neural Networks" of S. Haykin, and "Pattern Recognition and Machine Learning",of C. Bishop.I think all these three books are excellent,in their own way,and should not be missed from the bookshelf of anyone that copes with the Pattern Recognition field,either student or researcher.However, for the reader interested in developing computer algorithms in the Pattern Recognition area,the book of Theodoridis and Koutroubas is the superior choice.

The book describes the field, including classification and clustering, clearly and concisely, while not ignoring the key mathematical concepts. I'm a CS grad student studying this area and have been subjected to a number of textbooks that are math-heavy and fail to give any descriptive context of what's being presented. A good textbook on a subject should actually TEACH the reader the concepts. This one does that quite well. In addition, three chapters on feature generation and processing are included, a subject most other texts barely cover at all. This revised addition is a substantial expansion of the previous one and now includes many recently-developed concepts. If I were teaching an advanced undergrad or graduate course on the subject I would probably choose this as my primary text.

Awesome book! I really love it. Mathematics in this books are pretty easy, and the exposition of each topic is magnificent. Also it gives a lot of references which are useful for the practicant and the researcher.

This book is loaded with info. Definitely all I could handle for a master's-level CS class

My graduate Statistical Machine Learning course required me to purchase 2 books!...this one and another (not naming the other here). This book does SUCH A BETTER JOB at explaining the subtle assumptions the equations are making. My other book makes tons of assumptions making it easy to get lost :(. This book saved my butt in this class (no pun intended)!

Man, this IS the book on pattern recognition! Lengthy, simple, direct, clean; contains the most essential one must know about all the techniques when working with pattern recognition. I have also Duda et al Pattern Classification. But THIS one is far better and far didactic. If you want to learn how to classify patterns, this is THE book.

Although there is a TON of info in this book it's really not that great for learning pattern recognition. It's definitely more of a reference than anything else. You can't really read a section and then sit down at your computer and code it up. There a so many details missing. And the equations are so compact that you spend most your time decoding bad notation. If this book were a piece of software it would suffer from feature bloat. If you need to actually do any real applications using the techniques in this book you should definitely by the MATLAB companion text.

I agree with previous reviewers about the broadth and depth of the material in this book. Yes, i didn't read everything but the topics i was looking for were briefly and clearly explained. Exactly what is needed for an phD student doing his work in this field.I am a phD candidate in computer vision lab doing the research on image based localization.