"…it provides a good introduction to the subject of Pattern Classification." (Journal of Classification, September 2007)
"…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)
...provides information needed to choose the most appropriate of the many available technique for a given class of problems.
The first edition of this book, published 30 years ago by Duda and Hart, has been a defining book for the field of Pattern Recognition. Stork has done a superb job of updating the book. He has undertaken a monumental task of sifting through 30 years of material in a rapidly growing field and presented another snapshot of the field, determining what will be of importance for the next 30 years and incorporating it into this second edition. The style is easy to read as in the original book and the statistical, mathematical material comes alive with many new illustrations. The end result is harmonious, leading the reader through many new topics...
Pattern recognition systems play a role in applications as diverse as speech recognition, optical character recognition, image processing, and signal analysis. This reference provides information needed to choose the most appropriate of the many available techniques for a given class of problems. The latest edition includes explanations of classical and new methods, including neural networks, stochastic methods, genetic algorithms, and theory of learning. It provides algorithms to explain specific pattern-recognition and learning techniques as well as appendices covering the necessary mathematical background. Annotation c. Book News, Inc., Portland, OR (booknews.com)