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Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable

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樓主
發(fā)表于 2025-3-21 16:56:41 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Neural Information Processing
副標題30th International C
編輯Biao Luo,Long Cheng,Chaojie Li
視頻videohttp://file.papertrans.cn/664/663592/663592.mp4
叢書名稱Communications in Computer and Information Science
圖書封面Titlebook: Neural Information Processing; 30th International C Biao Luo,Long Cheng,Chaojie Li Conference proceedings 2024 The Editor(s) (if applicable
描述The nine-volume set constitutes the refereed proceedings of the 30th International Conference on Neural Information Processing, ICONIP 2023, held in Changsha, China, in November 2023.??.The?1274?papers presented in the proceedings set were carefully reviewed and selected from?652?submissions.?.The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements..
出版日期Conference proceedings 2024
關鍵詞Bioinformatics; Brain-machine interface; Computational finance; Computational intelligence; Control and
版次1
doihttps://doi.org/10.1007/978-981-99-8138-0
isbn_softcover978-981-99-8137-3
isbn_ebook978-981-99-8138-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
issn_series 1865-0929
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
The information of publication is updating

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沙發(fā)
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Neural Information Processing978-981-99-8138-0Series ISSN 1865-0929 Series E-ISSN 1865-0937
板凳
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地板
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https://doi.org/10.1007/978-981-99-8138-0Bioinformatics; Brain-machine interface; Computational finance; Computational intelligence; Control and
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發(fā)表于 2025-3-22 09:03:43 | 只看該作者
Differential Private (Random) Decision Tree Without Adding Noiseeached practical level due to many challenges on balancing privacy preservation and performance. In this paper, we propose a method of applying privacy preservation to the (random) decision tree, which is a variation of the expanded decision tree proposed by Fan et al. in 2003, to achieve the following goals:
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ndependently developed modules should be cultivated, and will play an important tactical role, but should not be considered a strategic target, as should instead be that of identifying a core language (a few core languages, each) accompanied by several compatible sub-languages. The reason is that th
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Tianxiang Xu,Chang Liu,Kun Zhang,Jianlin Zhang We group the relevant factors into three dimensions: meta-process phases, process elements, and the tool/user views. In the first dimension, we focus on enactable process models. For such models, we describe the design alternatives for a core process modelling language and a set of tailored sub-lan
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