Pattern Recognition
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This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for professionals and students of engineering. The authors, leading experts in the field of pattern recognition, have provided an up-to-date, self-contained volume encapsulating this wide spectrum of information. The very latest methods are incorporated in this edition: semi-supervised learning, combining clustering algorithms, and relevance feedback. · Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques· Many more diagrams included--now in two color--to provide greater insight through visual presentation· Matlab code of the most common methods are given at the end of each chapter.· More Matlab code is available, together with an accompanying manual, via this site · Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms.· An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary, and solved examples including real-life data sets in imaging, and audio recognition. The companion book will be available separately or at a special packaged price (ISBN: 9780123744869).
Key Features
- Thoroughly developed to include many more worked examples to give greater understanding of the various methods and techniques
- Many more diagrams included--now in two color--to provide greater insight through visual presentation
- Matlab code of the most common methods are given at the end of each chapter
- An accompanying book with Matlab code of the most common methods and algorithms in the book, together with a descriptive summary and solved examples, and including real-life data sets in imaging and audio recognition. The companion book is available separately or at a special packaged price (Book ISBN: 9780123744869. Package ISBN: 9780123744913)
- Latest hot topics included to further the reference value of the text including non-linear dimensionality reduction techniques, relevance feedback, semi-supervised learning, spectral clustering, combining clustering algorithms
- Solutions manual, powerpoint slides, and additional resources are available to faculty using the text for their course. Register at www.textbooks.elsevier.com and search on "Theodoridis" to access resources for instructor
Readership
Electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning, R&D engineers and university researchers in image and signal processing/analyisis, and computer vision
Table of Contents
- 1. Introduction
2. Classifiers based on Bayes Decision
3. Linear Classifiers
4. Nonlinear Classifiers
5. Feature Selection
6. Feature Generation I: Data Transformation and Dimensionality Reduction
7. Feature Generation II
8. Template Matching
9. Context Depedant Clarification
10. System Evaultion
11. Clustering: Basic Concepts
12. Clustering Algorithms: Algorithms L Sequential
13. Clustering Algorithms II: Hierarchical
14. Clustering Algorithms III: Based on Function Optimization
15. Clustering Algorithms IV: Clustering
16. Cluster Validity
Product details
- No. of pages: 984
- Language: English
- Copyright: © Academic Press 2008
- Published: October 20, 2008
- Imprint: Academic Press
- eBook ISBN: 9780080949123
- Hardcover ISBN: 9781597492720
About the Authors
Konstantinos Koutroumbas
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.
Affiliations and Expertise
Institute for Space Applications & Remote Sensing, National Observatory of Athens, Greece
Sergios Theodoridis
Sergios Theodoridis is professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China.
He has received a number of prestigious awards, including the 2014 IEEE Signal Processing Magazine Best Paper Award, the 2009 IEEE Computational Intelligence Society Transactions on Neural Networks Outstanding Paper Award, the 2017 European Association for Signal Processing
(EURASIP) Athanasios Papoulis Award, the 2014 IEEE Signal Processing Society Education Award, and the 2014 EURASIP Meritorious Service Award. He has served as president of EURASIP and vice president for the IEEE Signal Processing Society and as Editor-in-Chief IEEE Transactions on Signal processing. He is a Fellow of EURASIP and a Life Fellow of IEEE.
He is the coauthor of the best selling book Pattern Recognition, 4th edition, Academic Press, 2009 and of the book Introduction to Pattern Recognition: A MATLAB Approach, Academic Press, 2010.
Affiliations and Expertise
professor of machine learning and signal processing with the National and Kapodistrian University of Athens, Athens, Greece and with the Chinese University of Hong Kong, Shenzhen, China.