Pattern Classification (2nd Edition) (2nd Edition)

Author: Dida
Publisher:
Publish Date: 2003-09-01
Features: Practitioners in the development and research of pattern recognition systems, whether their applications involve speech recognition, character recognition, image processing, or signal analysis, often face the challenge of selecting from a bewildering array of technologies. This unique textbook and professional reference book provides you with ample materials and information to choose the right technology. As a new edition of a classic in the field of pattern recognition over the past few decades, this version updates and expands the original work, focusing on pattern classification and the significant advancements in the field in recent years. The book has been adopted as a textbook by over 120 universities, including Carnegie Mellon, Harvard, Stanford, and Cambridge. As a popular and classic textbook and professional reference, it primarily targets graduate students and technical professionals in fields such as electronic engineering, computer science, mathematics and statistics, media processing, pattern recognition, computer vision, artificial intelligence, and cognitive science. The first edition of this book, Pattern Classification and Scene Analysis, was published in 1973 and is a foundational classic in the fields of pattern recognition and scene analysis. In the second edition, in addition to retaining the main content of statistical pattern recognition and structural pattern recognition from the first edition, readers will find many new theories and methods added over the past 25 years, including neural networks, machine learning, data mining, evolutionary computation, invariance theory, hidden Markov models, statistical learning theory, and support vector machines, among others. The author also points out the direction for the development of pattern recognition in the next 25 years. The book includes numerous examples, comparisons of various methods, abundant figures and tables, as well as many end-of-chapter exercises and computer exercises.