Pattern Recognition / Edition 3

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Overview

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. This volume's unifying treatment covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. It includes discussion of the latest techniques in wavelets, wavelet packets, and fractals. This book presents cutting-edge material on neural networks, and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.

Audience: Engineering professionals and students in communications engineering, automation, information handling, communications, (statistical) pattern recognition, speech and image processing, & machine learning.

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Editorial Reviews

Booknews
A textbook for a graduate or undergraduate course of one or two semesters for students of such subjects as electrical and electronic engineering, computer engineering, computer science, informatics, and automation. Assumes a knowledge of basic calculus, elementary linear algebra, and some probability theory basics. Presents the underlying concepts and practices of pattern recognition from an engineering perspective, and describes applications in image analysis, speech processing, communications, and medical diagnosis. Annotation c. by Book News, Inc., Portland, Or.
From the Publisher
"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."- Dimitrios Gunopoulos, University of California, Riverside, USA.

"I cut my pattern recognition teeth on a draft version of Duda and Hart (1973). Over subsequent decades, I consistently did two things: (i) recommended Duda and Hart as the best book available on pattern recognition; and (ii) wanted to write the next best book on this topic.

I stopped (i) when the first edition ofS. Theodoridis andK. Koutroumbas'book appeared, and it supplanted the need for (ii)

It was, and is, the best book that has been written on the subject since Duda and Hart's seminal original text. Buy it - you'll be happy you did." - Jim Bezdek, University of West Florida and Senior Fellow, U. of Melbourne (Australia).

"I consider the fourth edition of the book Pattern Recognition, by S. Theodoridis and K. Koutroumbas as the "Bible of Pattern Recognition"- Simon Haykin, McMaster University, Canada

"I have taught a graduate course on statistical pattern recognition for more than twenty five years during which I have used many books with different levels of satisfaction. Recently, I adopted the book by Theodoridis and Koutroumbas (4th edition) for my graduate course on statistical pattern recognition at University of Maryland. This course is taken by students from electrical engineering, computer science, linguistics and applied mathematics. The comprehensive book by Thedoridis and Koutroumbas covers both traditional and modern topics in statistical pattern recognition in a lucid manner, without compromising rigor. This book elegantly addresses the needs of graduate students from the different disciplines mentioned above. This is the only book that does justice to both supervised and unsupervised (clustering) techniques. Every student, researcher and instructor who is interested in any and all aspects of statistical pattern recognition will find this book extremely satisfying. I recommend it very highly." -Rama Chellappa, University of Maryland

"The book Pattern Recognition, by Profs. Sergios Theodoridis and Konstantinos Koutroumbas, has rapidly become the "bible" for teaching and learning the ins and outs of pattern recognition technology. In my own teaching, I have utilized the material in the first four chapters of the book (from basics to Bayes Decision Theory to Linear Classifiers and finally to Nonlinear Classifiers) in my class on fundamentals of speech recognition and have found the material to be presented in a clear and easily understandable manner, with excellent problems and ideas for projects. My students have all learned the basics of pattern recognition from this book and I highly recommend it to any serious student in this area." -Prof. Lawrence Rabiner

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Product Details

  • ISBN-13: 9780123695314
  • Publisher: Elsevier Science
  • Publication date: 3/10/2006
  • Edition description: Older Edition
  • Edition number: 3
  • Pages: 856
  • Product dimensions: 6.00 (w) x 9.10 (h) x 1.60 (d)

Meet the Author

Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.

Konstantinos Koutroumbas acquired a degree from the University of Patras, Greece in Computer Engineering and Informatics in 1989, a MSc in Computer Science from the University of London, UK in 1990, and a Ph.D. degree from the University of Athens in 1995. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.

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Table of Contents

Ch. 1 Introduction 1
Ch. 2 Classifiers based on Bayes decision theory 13
Ch. 3 Linear classifiers 69
Ch. 4 Nonlinear classifiers 121
Ch. 5 Feature selection 213
Ch. 6 Feature generation I : linear transforms 263
Ch. 7 Feature generation II 327
Ch. 8 Template matching 397
Ch. 9 Context-dependent classification 427
Ch. 10 System evaluation 471
Ch. 11 Clustering : basic concepts 483
Ch. 12 Clustering algorithms I : sequential algorithms 517
Ch. 13 Clustering algorithms II : hierarchical algorithms 541
Ch. 14 Clustering algorithms III : schemes based on function optimization 589
Ch. 15 Clustering algorithms IV 653
Ch. 16 Cluster validity 733
App. A Hints from probability and statistics 785
App. B Linear algebra basics 797
App. C Cost function optimization 801
App. D Basic definitions from linear systems theory 819
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