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Titlebook: Rough Sets; International Joint Sheela Ramanna,Chris Cornelis,Davide Ciucci Conference proceedings 2021 Springer Nature Switzerland AG 202

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書目名稱Rough Sets
副標(biāo)題International Joint
編輯Sheela Ramanna,Chris Cornelis,Davide Ciucci
視頻videohttp://file.papertrans.cn/832/831898/831898.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Rough Sets; International Joint  Sheela Ramanna,Chris Cornelis,Davide Ciucci Conference proceedings 2021 Springer Nature Switzerland AG 202
描述.The volume LNAI 12872 constitutes the proceedings of the International Joint Conference on Rough Sets, IJCRS 2021, Bratislava, Slovak Republic, in September 2021. The conference was held as a hybrid event due to the COVID-19 pandemic...The 13 full paper and 7 short papers presented were carefully reviewed and selected from 26 submissions, along with 5 invited papers. The papers are grouped in the following topical sections: core rough set models and methods, related methods and hybridization, and areas of applications..
出版日期Conference proceedings 2021
關(guān)鍵詞artificial intelligence; attribute reduction; clustering algorithms; computer systems; computer vision; d
版次1
doihttps://doi.org/10.1007/978-3-030-87334-9
isbn_softcover978-3-030-87333-2
isbn_ebook978-3-030-87334-9Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2021
The information of publication is updating

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General Rough Modeling of Cluster Analysislusterings is invented, and this opens the subject to clearer conceptions and contamination-free theoretical proofs. Numeric ideas of validation are also proposed to be replaced by those based on general rough approximation. The essential approach is explained in brief and supported by an example.
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Possible Coverings in Incomplete Information Tables with Similarity of Valuesaximum possible ones which are derived from the minimum and the maximum possible indiscernibility relations that are equal to the intersection and the union of those from possible tables. The approximations are equal to those derived using the minimum and the maximum possibly indiscernible classes.
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DDAE-GAN: Seismic Data Denoising by Integrating Autoencoder and Generative Adversarial Networkdge of complex unknown noise. In this paper, for seismic denoising, we propose a new method with three techniques to handle them effectively. First, a Generative Adversarial Network (GAN) is employed to generate a large number of paired clean-noisy data using real noise. Second, a deep denoising aut
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