Genetic Programming for Image Classification
Language: English
Published by Springer International Publishing, 2021
Series: Book 11 of 12 - Adaptation, Learning, and Optimization
- Hardcover
- New
Condition: New
£ 118.25
Quantity: Over 20 available
Add to basketItem description from seller
Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Introduces a series of typical Genetic Programming-based approaches to feature learning in image classification Provides broad perceptive insights on what and how Genetic Programming can offer and shows a comprehensive and systematic resear.
Seller Inventory # 448686412
- Title
- Genetic Programming for Image Classification
- Author
- Ying Bi|Bing Xue|Mengjie Zhang
- Publisher
- Springer International Publishing
- Publication year
- 2021
- Condition
- New
- Binding
- Gebunden
- Language
- English
- ISBN 10
- 3030659267
- ISBN 13
- 9783030659264
- Series
- Book 11 of 12: Adaptation, Learning, and Optimization
- Seller catalogs
- Mathematik/Naturwissenschaften/Technik/Medizin
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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