Pattern Recognition (Third Edition in English)

Author: Theodoridis
Publisher:
Publish Date: 2006-09-01
Features: This book comprehensively covers the classic and current theories and practices of supervised and unsupervised pattern recognition, establishing a complete foundational knowledge system for professionals and university students. Co-authored by two top experts in the field of pattern recognition, the book elaborates on the applications of pattern recognition from an engineering perspective. It includes topics such as Bayesian classification, Bayesian networks, linear and nonlinear classifiers (including neural networks and support vector machines), dynamic programming, and hidden Markov models for sequential data. The book also covers features (including wavelets, principal component analysis, independent component analysis, and fractal analysis), feature selection techniques, fundamental concepts from learning theory, clustering concepts and algorithms, among others. This renowned worldwide classic is both comprehensive and relatively self-contained, offering introductions to foundational knowledge, updates on current research trends, and prospects for future developments. It is the most comprehensive reference in the field, adopted by numerous universities worldwide as a textbook. This book can serve as a textbook for graduate and senior undergraduate students in computer science, electronics, communications, automation, and related fields, as well as a reference for engineers in computer information processing and automatic control.