A comprehensive introduction to pattern recognition, covering fundamental concepts, algorithms, and applications.
Explores various pattern recognition techniques, including supervised and unsupervised learning, feature extraction, and classification.
Provides a detailed overview of different types of classifiers, such as linear discriminant analysis, support vector machines, and neural networks.
Discusses the principles of pattern recognition, including statistical pattern recognition, structural pattern recognition, and syntactic pattern recognition.
Includes real-world examples and case studies to illustrate the practical applications of pattern recognition.
Suitable for students, researchers, and practitioners in computer science, engineering, and related fields.
Written by leading experts in the field, providing authoritative and up-to-date information.
A valuable resource for anyone interested in understanding the fundamentals and applications of pattern recognition.
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