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Machine Learning, Optimization, and Data Science 9th International Conference, LOD 2023, Grasmere, UK, September 22–26, 2023, Revised Selected Papers, Part I /

This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023. The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. T...

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書誌詳細
団体著者: SpringerLink (Online service)
その他の著者: Nicosia, Giuseppe (編集者), Ojha, Varun (編集者), La Malfa, Emanuele (編集者), La Malfa, Gabriele (編集者), Pardalos, Panos M. (編集者), Umeton, Renato (編集者)
フォーマット: 電子媒体 eBook
言語:英語
出版事項: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
版:1st ed. 2024.
シリーズ:Lecture Notes in Computer Science, 14505
主題:
Information technology > Management.
Computer networks .
Electronic digital computers > Evaluation.
Computer systems.
Artificial intelligence.
Machine learning.
Computer Application in Administrative Data Processing.
Computer Communication Networks.
System Performance and Evaluation.
Computer System Implementation.
Artificial Intelligence.
Machine Learning.
オンライン･アクセス:https://doi.org/10.1007/978-3-031-53969-5
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その他の書誌記述
要約:This book constitutes the refereed proceedings of the 9th International Conference on Machine Learning, Optimization, and Data Science, LOD 2023, which took place in Grasmere, UK, in September 2023. The 72 full papers included in this book were carefully reviewed and selected from 119 submissions. The proceedings also contain 9 papers from and the Third Symposium on Artificial Intelligence and Neuroscience, ACAIN 2023. The contributions focus on the state of the art and the latest advances in the integration of machine learning, deep learning, nonlinear optimization and data science to provide and support the scientific and technological foundations for interpretable, explainable and trustworthy AI. .
物理的記述:XXII, 515 p. 193 illus., 152 illus. in color. online resource.
ISBN:9783031539695
ISSN:1611-3349 ;
DOI:10.1007/978-3-031-53969-5

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