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Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The

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發(fā)表于 2025-3-21 20:03:57 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱PRICAI 2024: Trends in Artificial Intelligence
副標(biāo)題21st Pacific Rim Int
編輯Rafik Hadfi,Patricia Anthony,Quan Bai
視頻videohttp://file.papertrans.cn/765/764625/764625.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The
描述.The five-volume proceedings set LNAI 15281-15285,?constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in?Kyoto, Japan, in November 18–24, 2024...The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions.?..The papers are organized in the following topical sections:..Part I:?Machine Learning,?Deep Learning..Part II:?Deep Learning,?Federated Learning,?Generative AI,?Natural Language Processing,?Large Language Models,?..Part III:?Large Language Models,?Computer Vision..Part IV:?Computer Vision,?Autonomous Driving,?Agents and Multiagent Systems,?Knowledge Graphs,?Speech Processing,?Optimization..Part V:?Optimization,?General Applications,?Medical Applications,?Theoretical Foundations of AI.
出版日期Conference proceedings 2025
關(guān)鍵詞Artificial Intelligence; Machine Learning; Deep Learning; Agents; Decision Theory; Large Language Models;
版次1
doihttps://doi.org/10.1007/978-981-96-0116-5
isbn_softcover978-981-96-0115-8
isbn_ebook978-981-96-0116-5Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Junjie Gao,Xiangyu Zheng,DongDong Wang,Zhixiang Huang,Bangqi Zhengand design scalability on modern graphics processing units (GPUs). These case studies serve as a guide to cryptographers exploring the design of efficient cryptographic implementations...?.978-981-10-1070-5Series ISSN 2367-3478 Series E-ISSN 2367-3486
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A Recursive Learning Algorithm for?the?Least Squares SVMby a smaller number of training samples than the standard LS-SVM, can recognize the checkerboard pattern accurately. The proposed LS-SVM model also demonstrated generalization performances superior?to other sparse models on five datasets from the UCI repository.
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