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派博传思国际中心 »期刊书目

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Titlebook: Genetic Programming for Image Classification; An Automated Approac Ying Bi,Bing Xue,Mengjie Zhang Book 2021 The Editor(s) (if applicable) a

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发表于 2025-3-21 16:17:05 | 显示全部楼层 |阅读模式
书目名称Genetic Programming for Image Classification
副标题An Automated Approac
编辑Ying Bi,Bing Xue,Mengjie Zhang
视频videohttp://file.papertrans.cn/383/382617/382617.mp4
概述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 sho
丛书名称Adaptation, Learning, and Optimization
图书封面Titlebook: Genetic Programming for Image Classification; An Automated Approac Ying Bi,Bing Xue,Mengjie Zhang Book 2021 The Editor(s) (if applicable) a
描述.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.   .. .
出版日期Book 2021
关键词Evolutionary Computation; Genetic Programming; Feature Learning; Image Classification; Computer Vision; M
版次1
doihttps://doi.org/10.1007/978-3-030-65927-1
isbn_softcover978-3-030-65929-5
isbn_ebook978-3-030-65927-1Series ISSN 1867-4534 Series E-ISSN 1867-4542
issn_series 1867-4534
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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发表于 2025-3-21 23:54:14 | 显示全部楼层
Evolutionary Computation and Genetic Programming, describes the basics of genetic programming, including representation, functions, terminals, population initialisation, genetic operators, and strongly typed genetic programming, in detail. Finally, it reviews typical works on genetic programming for feature learning.
集中营
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发表于 2025-3-22 02:34:17 | 显示全部楼层
SCORE
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发表于 2025-3-22 07:19:32 | 显示全部楼层
谆谆教诲
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发表于 2025-3-22 11:50:47 | 显示全部楼层
GP with Image Descriptors for Learning Global and Local Features,ariations. These image descriptors can be used to extract two types of image features, i.e., global features and local features. But domain expertise is often needed to determine what features are extracted. This chapter proposes a new feature learning approach using GP to automatically select and c
向外
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发表于 2025-3-22 13:26:20 | 显示全部楼层
GP with Image-Related Operators for Feature Learning,fective feature learning. However, this has not been extensively investigated in GP due to the limitations of the current GP representations. This chapter proposes a new GP-based approach with a flexible program structure and a number of image-related operators for feature learning in image classifi
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发表于 2025-3-22 20:24:57 | 显示全部楼层
GP for Simultaneous Feature Learning and Ensemble Learning,assification often need many manually settings and extensive human intervention on feature extraction, base learner selection and combination. Automating the processes of feature extraction and ensemble building can address this issue. This chapter proposes a GP-based approach with a new representat
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发表于 2025-3-22 22:11:52 | 显示全部楼层
arsenal
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发表于 2025-3-23 02:19:30 | 显示全部楼层
https://doi.org/10.1007/978-3-030-65927-1Evolutionary Computation; Genetic Programming; Feature Learning; Image Classification; Computer Vision; M
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发表于 2025-3-23 05:37:16 | 显示全部楼层
Dick Mul,Ingrid Bliek,Katja ZuurThis chapter provides a summary of the book. This chapter revisits the main GP-based approaches presented in the book and summaries the major conclusions. It also highlights several key research directions to encourage future work.
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