Pattern Recognition
- 5th Edition - September 1, 2029
- Latest edition
- Authors: Konstantinos Koutroumbas, Sergios Theodoridis
- Language: English
- Hardback ISBN:9 7 8 - 0 - 1 2 - 8 0 1 1 8 6 - 7
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 0 1 2 6 8 - 0
This book considers classical and current theory and practice, of supervised, unsupervised and semi-supervised pattern recognition, to build a complete background for pr… Read more
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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
- 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.
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
1. Introduction2. Classifiers based on Bayes Decision 3. Linear Classifiers4. Nonlinear Classifiers5. Feature Selection6. Feature Generation I: Data Transformation and Dimensionality Reduction7. Feature Generation II8. Template Matching 9. Context Depedant Clarification10. System Evaultion11. Clustering: Basic Concepts12. Clustering Algorithms: Algorithms L Sequential 13. Clustering Algorithms II: Hierarchical 14. Clustering Algorithms III: Based on Function Optimization 15. Clustering Algorithms IV: Clustering16. Cluster Validity
- Edition: 5
- Latest edition
- Published: September 1, 2029
- Language: English
KK
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, GreeceST
Sergios Theodoridis
Sergios Theodoridis is professor emeritus of machine learning and data processing with the National and Kapodistrian University of Athens, Athens, Greece. He has also served as distinguished professor with the Aalborg University Denmark and as professor with the Chinese University of Hong Kong, Shenzhen, China. In 2023, he received an honorary doctorate degree (D.Sc) from the University of Edinburgh, U.K. He has also 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 Carl Friedrich Gauss 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. He is a Fellow of EURASIP and a Life Fellow of IEEE. He is the coauthor of the 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, National and Kapodistrian University of Athens, Athens, Greece