From Book News, Inc.
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.Book News, Inc.®, Portland, OR
Review
"...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) "...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002) "...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)
Review
"...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) "This book is the unique text/professional reference for any serious student or worker in the field of pattern recognition." (Mathematical Reviews, Issue 2001k) "...strongly recommended both as a professional reference and as a text for students..." (Technometrics, February 2002) "...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)
Technometrics, February 2002
"...strongly recommended both as a professional reference and as a text for students..."
Zentralblatt MATH, Vol. 968, 2001/18
"...gives a systematic overview about the major topics in pattern recognition, based whenever possible on fundamental principles."
Journal of Classification, Vol.18, No.2 2001
"attractively presented and readable"
Book Description
The first edition, published in 1973, has become a classic reference in the field. Now with the second edition, readers will find information on key new topics such as neural networks and statistical pattern recognition, the theory of machine learning, and the theory of invariances. Also included are worked examples, comparisons between different methods, extensive graphics, expanded exercises and computer project topics.
From the Back Cover
From the reviews . . . "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..." –Sargur N. Srihari, PhD, Director, Center for Excellence in Document Analysis and Recognition, Distinguished Professor, Department of Computer Science and Engineering, SUNY at Buffalo Practitioners developing or investigating pattern recognition systems in such diverse application areas as speech recognition, optical character recognition, image processing, or signal analysis, often face the difficult task of having to decide among a bewildering array of available techniques. This unique text/professional reference provides the information you need to choose the most appropriate method for a given class of problems, presenting an in-depth, systematic account of the major topics in pattern recognition today. A new edition of a classic work that helped define the field for over a quarter century, this practical book updates and expands the original work, focusing on pattern classification and the immense progress it has experienced in recent years. Special features include: - Clear explanations of both classical and new methods, including neural networks, stochastic methods, genetic algorithms, and theory of learning
- Over 350 high-quality, two-color illustrations highlighting various concepts
- Numerous worked examples
- Pseudocode for pattern recognition algorithms
- Expanded problems, keyed specifically to the text
- Complete exercises, linked to the text
- Algorithms to explain specific pattern-recognition and learning techniques
- Historical remarks and important references at the end of chapters
- Appendices covering the necessary mathematical background
NOTE: Computer Manual in MATLAB to Accompany Pattern Classification, 2e users access toolbox via ftp://ftp.wiley.com/public/sci_tech_med/pattern_classification/ (Note: Visitors will require a password from the Manual to access.)
About the Author
RICHARD O. DUDA, PhD, is Professor in the Electrical Engineering Department at San Jose State University, San Jose, California. PETER E. HART, PhD, is Chief Executive Officer and President of Ricoh Innovations, Inc. in Menlo Park, California. DAVID G. STORK, PhD, is Chief Scientist, also at Ricoh Innovations, Inc. Rating 4.0
Overall a Good BookOverall this is a good book on the field. There is plenty of examples and covers alot of topics (from Bayesian estimation, Support Vector Machines, LDA, PCA, Neural Networks etc.). My only disappointment with this book was in it's coverage of Hidden Markov Models. The algorith it presents is very confusing, and you are better off reading the Rabiner tutorial on the subject if you want to learn more or implement HMMs.Excellent reference bookI found book very useful. Figures, mathematical explanations and algortihms provides complemantry information to understand topic better. There may some errors in the book but I did not found any fatal one. Problem questions of each chapter are very useful. This is a must book whom are interesting in pattern classification area both in industry and academy.
Pattern ClassificationI found this book quite useful as an augmentive text to Elements of Statistical Learning used in a grad engineering level data mining course. This book is written more at an engineering level, and I found it to bridge well between advanced texts such as Elements of Statistical Learning and more general audience books that really are lacking. Duda and Hart do a good job at explaining the concepts, however some techniques only recieve a cursory overview while other topics are rather elaborated upon, however this may have been done by the authors experience of which techniques are commonly employed in practice. The excercises at the end of the chapters include a lot of hands on programming and computer-based assignments which I found useful, and a MATLAB workbook associated with this is also offered, however I have not read this book. Nonetheless I have implemented some of the concepts in this book using Matlab and it definately does help to cement the idea, even if this is just serves as an intellectual excercise and isn't intended to be used for anything else. With a little bit of digging through the help or using a book such as Ripley and Venerable's Modern Applied Statistics with S, most if not all of the techniques can be explored using the R statistical software. |
