Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. This volume's unifying treatment covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. It includes discussion of the latest techniques in wavelets, wavelet packets, and fractals. This book presents cutting-edge material on neural networks, and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct result of more than 10 years of teaching experience, the text was developed by the authors through use in their own classrooms.
Key Features
* Covers the entire spectrum of pattern recognition applications
* Includes discussion of the latest techniques in wavelets, wavelet packets, and fractals
* Presents cutting-edge material on neural networks
* Enhances student motivation
* Approaches pattern recognition from the designer's point of view
"synopsis" may belong to another edition of this title.
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.
Pattern recognition is becoming increasingly important in the age of automation and information handling and retrieval.
This book provides the most comprehensive treatment available of pattern recognition, from an engineering perspective. Developed through more than ten years of teaching experience, Pattern Recognition is appropriate for both advanced engineering students and practicing engineers.
Coverage includes:
· Feature generation, including features based on Wavelet Transforms and Fractals.
· Feature selection techniques.
· Design of linear and non linear classifiers, including Bayesian, Multilayer Perceptrons, and RBF networks
. Context-dependent classification, including Dynamic Programming and Hidden Markov Modeling techniques
. Classical approaches, as well as more recent developments in clustering algorithms, such as fuzzy, possibilistic, morphological, genetic, and annealing techniques
. Coverage of numerous, diverse applications, including Image Analysis, Character Recognition, Medical Diagnosis, Speech Recognition, and Channel Equalization
. Numerous computer simulation examples, supporting the methods given in the book, available via the Webb.
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