by Menahem Friedman (Nuclear Research Center-Negv, Israel) & Abraham Kandel (University of South Florida, USA & Tel-Aviv University, Israel)

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester course in discrete mathematics and a knowledge of the basic preliminaries of calculus, linear algebra and probability theory.

• Decision Functions
• Classification by Distance Functions and Clustering
• Classification Using Statistical Approach
• Feature Selection
• Fuzzy Classification and Pattern Recognition
• Syntactic Pattern Recognition
• Neural Nets and Pattern Classification