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Rating: 
Summary: Supplementary.
Review: "Thus researchers from all walks of pattern recognition should get something out of this book."I agree.
Rating:
Summary: Supplementary.
Review: "Thus researchers from all walks of pattern recognition should get something out of this book." I agree.
Rating:
Summary: Continuing...
Review: I am working on a project and I am constantly getting inspired from this book. This book seems to have the practical power of Rogers(Computer Graphics) writting while keeping the theoretical dichipline. So you can safetly combine algorithms and be sure that you are walking on a correct path, simply buy this book all of you who are fed up with a book fool of formulas and "chatting" without practise it will probably save you from a lot of searching. Thats the end of my review. I think I said enough good things and a little criticism on this book.
Rating:
Summary: An excellent book for pattern recognition
Review: I think the authors provide a nice balance between theory and practice. On one hand, the algorithms presented can and are meant to be implemented for testing. On the other hand, the authors provide a fairly sound mathematical treatment of areas such as Markov Models, clustering, and template matching. Most important, the authors do not focus attention only on one type of problem (e.g. character recognition). Thus researchers from all walks of pattern recognition should get something out of this book. Two big thumbs up!
Rating:
Summary: An excellent book for pattern recognition
Review: I think the authors provide a nice balance between theory and practice. On one hand, the algorithms presented can and are meant to be implemented for testing. On the other hand, the authors provide a fairly sound mathematical treatment of areas such as Markov Models, clustering, and template matching. Most important, the authors do not focus attention only on one type of problem (e.g. character recognition). Thus researchers from all walks of pattern recognition should get something out of this book. Two big thumbs up!
Rating:
Summary: Good Job...
Review: Nice Job. I was (un)lucky to have these fellows as teachers. What they tell in this book is a refined version of the truth...everything "magic" becomes not "magic" if only you can unravel its mysteries, see the limitations and possibly design sth with better bandwidth and then be honest and help others to see the limitations etc...etc. Well mr.Thodoridis "harem" :-) should be proud of him. But still this book could have been better. This is because because mathematics are just numbers if they do not speak to you. Do not expect to find the magic of "Luenberger" or "Brigham" inside this book. But a honest and up to date investigation of theory refined with practise. Also they could have been more illustrative. For example chapter 2 Page 18 at the end. Actualy the pdf of is "shrinking" by a factor L21/L12 < 1. Then draw the pdf on figure 2_1 with dashed lines and then show that xo is moved to the left. This is what I call that maths are speaking to you... Anyway you can not find a person who is perfect as this would mean a signal with a band equal to 1 with IFT equal to a Dirac line ... simply impossible! :-) Buy this book, but I really sugest that it should be studied in an "academic" enviroment. See the quotes I make is just to state the fact that in these times we are living everything is possible...
Rating: 
Summary: Short description of the book
Review: Pattern Recognition is becoming increasingly important in the age of automation and information handling and retrieval. This book provides the most comprehensive treatment available of pattern recognition, from an engineering perspective. Developed through more than ten years of teaching experience, PATTERN RECOGNITION is appropriate for both advanced engineering students and practicing engineers. Coverage Includes: - Feature generation, including features based on Wavelet Transforms and Fractals - Feature selection techniques - Design of linear and non-linear classifiers, including Bayesian, Multilayer Perceptrons, and RBF networks - Context-dependent classification, including Dynamic Programming and Hidden Markov Modelling techniques - Classical approaches, as well as more recent developments in clustering algorithms, such as fuzzy, possibilistic, morphological, genetic, and annealing techniques - Coverage of numerous, diverse applications, including Image Analysis, Character Recognition, Medical Diagnosis, Speech Recognition, and Channel Equalization - Numerous Computer simulation examples, supporting the methods given in the book, available via the Web.
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