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Review
"…a fantastic book! The presentation...could not be better, and I recommend that future authors consider…this book as a role model." (Journal of Statistical Computation and Simulation, March 2006)
"...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002)
"...provides information needed to choose the most appropriate of the many available technique for a given class of problems." (SciTech Book News, Vol. 25, No. 2, June 2001)
"I do not believe anybody wishing to teach or do serious work on Pattern Recognition can ignore this book, as it is the sort of book one wishes to find the time to read from cover to cover!" (Pattern Analysis & Applications Journal, 2001)
"This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." (Mathematical Reviews, Issue 2001k)
"...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles." (Zentralblatt MATH, Vol. 968, 2001/18)
"attractively presented and readable" (Journal of Classification, Vol.18, No.2 2001)
Pattern Analysis & Applications Journal, 4th July 2001
Bayesian decision theory is a fundamental statistical approach to the problem of pattern classification. Read the first page
Concordance
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover
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Most helpful customer reviews
5.0 out of 5 stars
Definite Page-turner!,
By Peter Stempel (Florence, Italy) - See all my reviews
This review is from: Pattern Classification (Hardcover)
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'.
1.0 out of 5 stars
Disapointing,
By
This review is from: Pattern Classification (Hardcover)
There are not many solved examples to learn from; at the end of each chapter there are useless exercises since there are no answers. While I was a pattern recognition student, even though the professor adopted this book, I based myself on other books to learn pattern recognition.
4.0 out of 5 stars
Excellent Introductory Text and Reference Tool,
By
This review is from: Pattern Classification (Hardcover)
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.
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