Synopses & Reviews
A classic -- offering comprehensive and unified coverage with a balance between theory and practice!
Pattern recognition is integral to a wide spectrum of scientific disciplines and technologies including image analysis, speech recognition, audio classification, communications, computer-aided diagnosis, and data mining. The authors, leading experts in the field of pattern recognition, have once again provided an up-to-date, self-contained volume encapsulating this wide spectrum of information.
Each chapter is designed to begin with basics of theory progressing to advanced topics and then discusses cutting-edge techniques. Problems and exercises are present at the end of each chapter with a solutions manual provided via a companion website where a number of demonstrations are also available to aid the reader in gaining practical experience with the theories and associated algorithms.
This edition includes discussion of Bayesian classification, Bayesian networks, linear and nonlinear classifier design (including neural networks and support vector machines), dynamic programming and hidden Markov models for sequential data, feature generation (including wavelets, principal component analysis, independent component analysis and fractals), feature selection techniques, basic concepts from learning theory, and clustering concepts and algorithms. This book considers classical and current theory and practice, of both supervised and unsupervised pattern recognition, to build a complete background for professionals and students of engineering.
FOR INSTRUCTORS: To obtain access to the solutions manual for this title simply register on our textbook website (textbooks.elsevier.com)and request access to the Computer Science or Electronics and Electrical Engineering subject area. Once approved (usually within one business day) you will be able to access all of the instructor-only materials through the "Instructor Manual" link on this book's full web page.
* The latest results on support vector machines including v-SVM's and their geometric interpretation
* Classifier combinations including the Boosting approach
* State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
* Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
Review
"The book is written in a very readable, no-nonsense style. I found that there was just the right amount of text to describe a concept ... "
Larry O'Gorman, IAPR Newsletter, April 2006
Synopsis
Pattern recognition is a fast growing area with applications in a widely diverse number of fields such as communications engineering, bioinformatics, data mining, content-based database retrieval, to name but a few. This new edition addresses and keeps pace with the most recent advancements in these and related areas. This new edition: a) covers Data Mining, which was not treated in the previous edition, and is integrated with existing material in the book, b) includes new results on Learning Theory and Support Vector Machines, that are at the forefront of today's research, with a lot of interest both in academia and in applications-oriented communities, c) for the first time treats audio along with image applications since in today's world the most advanced applications are treated in a unified way and d) the subject of classifier combinations is treated, since this is a hot topic currently of interest in the pattern recognition community.
- The latest results on support vector machines including v-SVM's and their geometric interpretation
- Classifier combinations including the Boosting approach
- State-of-the-art material for clustering algorithms tailored for large data sets and/or high dimensional data, as required by applications such as web-mining and bioinformatics
- Coverage of diverse applications such as image analysis, optical character recognition, channel equalization, speech recognition and audio classification
Synopsis
95. Since 2001 he has been with the Institute for Space Applications and Remote Sensing of the National Observatory of Athens.
Synopsis
nd Westfield College of the University of London, UK in 1990, and a Ph.D. degree from the Department of Informatics and Telecommunications of 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.
About the Author
Sergios Theodoridis holds a physics degree from Athens University and an M.Sc. and a Ph.D. degree in electronics and electrical engineering from the University of Birmingham, UK He is currently with the Department of Informatics, University of Athens. His research interests lie in the areas of adaptive signal processing, communications, and pattern recognition. Theodoridis has published more than 40 papers in journals and more than 50 refereed conference papers, and he is the coeditor of Adaptive System Identification and Signal Processing Algorithms. Theodoridis was General Chairman for EUSIPCO â98 and Organizing Chairman of PARPLE 1995, plus he has served as an Associate Editor for IEEE Transactions on Signal Processing. He has held various consultancy posts both in Industry and the Greek Government, and he has participated in a number of European Union funded projects.Konstantinos Koustroumbas holds a computer engineering degree from the University of Patras, an M.Sc. degree from the University of London, UK, and a Ph.D. from Athens University. His research interests lie in the area of neural networks and pattern recognition. Koustroumbas is currently with the Hellenic Telecommunications Organization.
Table of Contents
Chapter 1: Introduction
Chapter 2: Classifiers Based on Bayes Decision Theory
Chapter 3: Linear Classifiers
Chapter 4: Nonlinear Classifiers
Chapter 5: Feature Selection
Chapter 6: Feature Generation I
Chapter 7: Feature Generation II
Chapter 8: Template Matching
Chapter 9: Context-Dependant Classification
Chapter 10: System Evaluation
Chapter 11: Clustering: Basic Concepts
Chapter 12: Clustering Algorithms I (Sequential)
Chapter 13: Clustering Algorithms II (Hierarchical)
Chapter 14: Clustering Algorithms III (Functional Optimization)
Chapter 15: Clustering Algorithms IV (Graph Theory)
Chapter 16: Cluster Validity