Pattern Recognition

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

Sergios Theodoridis acquired a Physics degree with honors from the University of Athens, Greece in 1973 and a MSc and a Ph.D. degree in Signal Processing and Communications from the University of Birmingham, UK in 1975 and 1978 respectively. Since 1995 he has been a Professor with the Department of Informatics and Communications at the University 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.

Introduction; Classifiers Based on Bayes Decision Theory; Linear Classifiers; Nonlinear Classifiers; Feature Selection; Feature Generation I; Feature Generation II; Template Matching; Context-Dependant Classification; System Evaluation; Clustering: Basic Concepts Clustering Algorithms I (Sequential); Clustering Algorithms II (Hierarchical); Clustering Algorithms III (Functional Optimization); Clustering Algorithms IV (Graph Theory); Cluster Validity