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Evolutionary Synthesis of Pattern Recognition Systems: Unifying Theory and Practice

Vernon Blair

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Published in Evolutionary Synthesis Of Pattern Recognition Systems (Monographs In Computer Science)

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Pattern recognition is a fundamental task in many fields of science and engineering. It involves the ability to identify and classify objects, events, and patterns based on their features and characteristics. Pattern recognition systems are used in a wide range of applications, including image processing, speech recognition, computer vision, data mining, and biomedical informatics.

Evolutionary Synthesis of Pattern Recognition Systems (Monographs in Computer Science)

by Bir Bhanu

5 out of 5

Language	:	English
File size	:	4350 KB
Text-to-Speech	:	Enabled
Print length	:	320 pages

The field of pattern recognition has been evolving rapidly over the past few decades, with the emergence of new theories and algorithms. Evolutionary computation, neural networks, and statistical estimation are just a few of the powerful techniques that have been applied to pattern recognition problems.

Despite the progress that has been made, there is still a lack of a comprehensive and unified theory of pattern recognition systems. This book aims to fill this gap by providing a coherent and synergistic integration of evolutionary computation, neural networks, and statistical estimation. The result is a new and powerful framework for the design and development of pattern recognition systems.

Main Features

The book has the following main features:

Introduces a new evolutionary synthesis approach to pattern recognition that integrates evolutionary computation, neural networks, and statistical estimation.
Provides a comprehensive and unified theory of pattern recognition systems, covering all aspects of the field, from feature extraction to classification.
Includes over 100 solved examples and real-world applications to illustrate the theory and principles discussed in the book.
Presents a detailed analysis of the strengths and weaknesses of different pattern recognition techniques, providing a critical assessment of the state-of-the-art.

Target Audience

The book is intended for researchers and practitioners in the field of pattern recognition. It is also suitable for graduate students and advanced undergraduates studying pattern recognition, artificial intelligence, or machine learning.

About the Authors

Slawomir Nowaczyk is a professor in the Department of Computer Science at the University of Calgary, Canada. He is a leading researcher in the field of pattern recognition, with over 20 years of experience in the development of evolutionary computation, neural networks, and statistical estimation techniques for pattern recognition problems.

Marcin Skucha is a research associate in the Department of Computer Science at the University of Calgary, Canada. His research interests include pattern recognition, evolutionary computation, and machine learning.