Pattern Recognition - (Third Edition)

Author: (Greece) Theodoridis
Publisher:
Publish Date: 2006-12-01
Features: Pattern recognition refers to the processing and analysis of various forms of information that represent things or phenomena, in order to describe, identify, classify, and interpret them. It is an important component of information science and artificial intelligence, with main application areas including image analysis and processing, speech recognition, sound classification, communication, computer-aided diagnosis, data mining, and other disciplines. Based on a perfect combination of current theory and practice, this book discusses Bayesian classification, Bayesian networks, the design of linear and nonlinear classifiers, dynamic programming, and hidden Markov models for sequential data, as well as feature generation, feature selection techniques, basic concepts of learning theory, and clustering concepts and algorithms. Compared to the previous edition, the main updates focus on support vector machines and clustering algorithms, with a particular emphasis on feature generation for image analysis, speech recognition, and sound classification. At the end of each chapter, exercises and problems are provided, and solutions to the exercises are available on the supporting website to help readers gain practical experience. This book can serve as a textbook for graduate students and senior undergraduate students in fields such as automation, computer science, electronics, and communication at universities and colleges, as well as a reference book for engineers and technicians in related fields such as computer information processing and automatic control.