Pattern Recognition

Pattern Recognition, Fourth Edition

Table of Contents:
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

About 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.

Review :
"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.nbsp; I stopped (i) when the first edition ofnbsp;S. Theodoridis andnbsp;K. Koutroumbas'nbsp;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