Genetic Programming for Image Classification: An Automated Approach to Feature Learning (Adaptation, Learning, and Optimization, 24, Band 24) - Hardcover

Bi, Ying; Xue, Bing; Zhang, Mengjie

Hardcover

ISBN 10: 3030659267 ISBN 13: 9783030659264

Verlag: Springer, 2021

Inhaltsangabe

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate andpostgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.   

 

Über diesen Titel

Über die Autorin bzw. den Autor

Abbas Jamalipour is the Professor of Ubiquitous Mobile Networking at the University of Sydney, Australia, and holds a PhD in Electrical Engineering from Nagoya University, Japan. He is a Fellow of the Institute of Electrical and Electronics Engineers (IEEE), the Institute of Electrical, Information, and Communication Engineers (IEICE), and the Institution of Engineers Australia; also an ACM Professional Member and an IEEE Distinguished Lecturer. He has authored nine technical books, eleven book chapters, over 450 technical papers, and five patents, all in the area of wireless and mobile communications. Dr. Jamalipour is an elected member of the Board of Governors, Executive Vice-President, Chair of Fellow Evaluation Committee, and the Editor-in-Chief of the Mobile World, of IEEE Vehicular Technology Society. He was the Editor-in-Chief IEEE Wireless Communications, Vice President-Conferences and a member of Board of Governors of the IEEE Communications Society, and has been an editor for several journals. He has been a General Chair or Technical Program Chair for a number of conferences, including IEEE ICC, GLOBECOM, WCNC and PIMRC. He is the recipient of a number of prestigious awards such as the 2016 IEEE ComSoc Distinguished Technical Achievement Award in Communications Switching and Routing, 2010 IEEE ComSoc Harold Sobol Award, the 2006 IEEE ComSoc Best Tutorial Paper Award, as well as 15 Best Paper Awards. Ying Bi received the Ph.D. degree in Electrical Engineering from the University of Sydney, Australia. She is currently a Research Associate in the School of Electrical and Information Engineering at the University of Sydney. Her current research interests include wireless powered communications, physical layer security in wireless communications, cyber security in smart grid communication networks, and the applications of game theory and optimization theory in these areas. She was a recipient of Australian Postgraduate Award and Norman I Price Supplementary Scholarship.

Von der hinteren Coverseite

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.   

 

Details zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2021
• Sprache: Englisch
• ISBN 10: 3030659267
• ISBN 13: 9783030659264
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 286
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