| Pattern Recognition, Fourth Edition |  | Authors: Sergios Theodoridis, Konstantinos Koutroumbas
Publisher: Academic Press
Category: Book
List Price: $99.95
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Rating:  5 reviews
Media: Hardcover
Edition: 4
Pages: 984
Number Of Items: 1
Shipping Weight (lbs): 3.7
Dimensions (in): 9.3 x 7.8 x 2.2
ISBN: 1597492728
Dewey Decimal Number: 621
EAN: 9781597492720
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Product Description
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback.
Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
Many more diagrams included--now in two color--to provide greater insight through visual presentation
Matlab code of the most common methods are given at the end of each chapter
An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913)
Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms
Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor.
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| Customer Reviews:
 Excellent book on pattern recognition July 9, 2009
Dimitrios Gunopulos (UCR, CA, USA)
4 out of 4 found this review helpful
This book is an excellent reference for pattern recognition, machine learning, and data mining. It focuses on the problems of classification and clustering, the two most important general problems in these areas. This book has tremendous breadth and depth in its coverage of these topics; it is clearly the best book available on the topic today.
The new edition is an excellent up-to-date revision of the book. I have especially enjoyed the new coverage provided in several topics, including new viewpoints on Support Vector Machines, and the complete in-depth coverage of new clustering methods.
This is a standout characteristic of this book: the coverage of the topics is solid, deep, and principled throughout. The book is very successful in bringing out the important points in each technique, while containing lots of interesting examples to explain complicated concepts. I believe the section on dimensionality reduction is an excellent exposition on this topic, among the best available, and this is just one example. Combined with a coverage unique in its extend, this makes the book appropriate for use as a reference, as a textbook for upper level undergraduate or graduate classes, and for the practitioner that wants to apply these techniques in practice.
I am a professor in Computer Science. Although pattern recognition is not my main focus, I work in the related fields of data mining and databases. I have used this book for my own research and, very successfully, as teaching material. I would strongly recommend this book to both the academic student and the professional.
Great book November 23, 2009
Antoin Baker (Tucson, AZ)
4 out of 4 found this review helpful
Probably the best book on Pattern Recognition available. While other "Pattern Classification" books simply bombard the reader with a huge variety of unrelated classifiers, this book actually covers the entire "Pattern Recognition" field, not just the classifiers.
Linear and nonlinear classifiers...check
Optimal Feature Selection...check
Feature Generation...check
Template Matching...check
Five substantial chapters on clustering...check
and so much more.
The book is huge, but worth the read. I also appreciate the fact that it has a DSP/image processing/computer vision bias; sort of like the first edition of Duda and Hart.
Great book.
Pattern Recognition - Clearly Written February 3, 2010
Gene Shuman (Fairfax, VA)
1 out of 1 found this review helpful
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.
Really a bible in pattern recognition March 17, 2010
Vladislavs Dovgalecs (Bordeaux, France)
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.
 It might be the bible for pattern recognition but ... June 5, 2010
Abel Brown (USA)
3 out of 3 found this review helpful
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.
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