Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Genetic Programming for Image Classification: An Automated Approach to Feature Learning

Genetic Programming for Image Classification: An Automated Approach to Feature Learning

Bi, Ying
Xue, Bing
Zhang, Mengjie

Paperback

Series: Adaptation, Learning, and Optimization, Book 24

Technology & EngineeringGeneral Computers

ISBN10: 3030659291
ISBN13: 9783030659295
Publisher: Springer Nature
Published: Feb 10 2022
Pages: 258
Weight: 0.90
Height: 0.60 Width: 6.14 Depth: 9.21
Language: English

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.

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