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Genetic programming for image classi...

Bi, Ying.

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Genetic programming for image classification = an automated approach to feature learning /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Genetic programming for image classification/ by Ying Bi, Bing Xue, Mengjie Zhang.
其他題名:	an automated approach to feature learning /
作者:	Bi, Ying.
其他作者:	Xue, Bing.
出版者:	Cham :Springer International Publishing : : 2021.,
面頁冊數:	xxviii, 258 p. :ill., digital ;24 cm.
內容註:	Computer Vision and Machine Learning -- Evolutionary Computation and Genetic Programming -- Multi-Layer Representation for Binary Image Classification -- Evolutionary Deep Learning Using GP with Convolution Operators -- GP with Image Descriptors for Learning Global and Local Features -- GP with Image-Related Operators for Feature Learning -- GP for Simultaneous Feature Learning and Ensemble Learning -- Random Forest-Assisted GP for Feature Learning -- Conclusions and Future Directions.
Contained By:	Springer Nature eBook
標題:	Genetic programming (Computer science) -
電子資源:	https://doi.org/10.1007/978-3-030-65927-1
ISBN:	9783030659271

Genetic programming for image classification = an automated approach to feature learning /
Bi, Ying.

Genetic programming for image classificationan automated approach to feature learning /[electronic resource] :by Ying Bi, Bing Xue, Mengjie Zhang. - Cham :Springer International Publishing :2021. - xxviii, 258 p. :ill., digital ;24 cm. - Adaptation, learning, and optimization,v.241867-4534 ;. - Adaptation, learning, and optimization ;v.24..

Computer Vision and Machine Learning -- Evolutionary Computation and Genetic Programming -- Multi-Layer Representation for Binary Image Classification -- Evolutionary Deep Learning Using GP with Convolution Operators -- GP with Image Descriptors for Learning Global and Local Features -- GP with Image-Related Operators for Feature Learning -- GP for Simultaneous Feature Learning and Ensemble Learning -- Random Forest-Assisted GP for Feature Learning -- Conclusions and Future Directions.

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.

ISBN: 9783030659271

Standard No.: 10.1007/978-3-030-65927-1doiSubjects--Topical Terms:

572479
Genetic programming (Computer science)

LC Class. No.: QA76.623

Dewey Class. No.: 006.31

Genetic programming for image classification = an automated approach to feature learning /
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520 $a 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.
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