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樓主
發(fā)表于 2025-3-21 19:32:29 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Granular Computing Based Machine Learning
編輯Han Liu,Mihaela Cocea
視頻videohttp://file.papertrans.cn/388/387855/387855.mp4
叢書名稱Studies in Big Data
圖書封面Titlebook: ;
出版日期Book 2018
版次1
doihttps://doi.org/10.1007/978-3-319-70058-8
isbn_softcover978-3-319-88884-2
isbn_ebook978-3-319-70058-8Series ISSN 2197-6503 Series E-ISSN 2197-6511
issn_series 2197-6503
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沙發(fā)
發(fā)表于 2025-3-21 23:00:45 | 只看該作者
Conclusion,granular computing based machine learning is inspired philosophically from real-life examples. Moreover, we suggest some further directions to extend the current research towards advancing machine learning in the future.
板凳
發(fā)表于 2025-3-22 03:51:27 | 只看該作者
Granular Computing Based Machine Learning978-3-319-70058-8Series ISSN 2197-6503 Series E-ISSN 2197-6511
地板
發(fā)表于 2025-3-22 07:14:15 | 只看該作者
https://doi.org/10.1007/978-3-658-40438-3ncepts of traditional data science are then explored to show the value of data. Furthermore, the concepts of machine learning and granular computing are provided in the context of intelligent data processing. Finally, the main contents of each of the following chapters are outlined.
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發(fā)表于 2025-3-22 10:57:15 | 只看該作者
Metaverse: Concept, Content and Contexttic learning, discriminative learning, single-task learning and random data partitioning. We also identify general issues of traditional machine learning, and discuss how traditional learning approaches can be impacted due to the presence of big data.
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發(fā)表于 2025-3-22 13:57:55 | 只看該作者
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發(fā)表于 2025-3-22 18:56:35 | 只看該作者
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發(fā)表于 2025-3-23 00:42:32 | 只看該作者
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發(fā)表于 2025-3-23 04:28:13 | 只看該作者
Peter Clark,Martin Best,Aurore Porsonf veracity and variability, respectively. In the sentiment analysis case study, we show the performance of fuzzy approaches on movie reviews data, in comparison with other commonly used non-fuzzy approaches.
10#
發(fā)表于 2025-3-23 07:35:22 | 只看該作者
Introduction,ncepts of traditional data science are then explored to show the value of data. Furthermore, the concepts of machine learning and granular computing are provided in the context of intelligent data processing. Finally, the main contents of each of the following chapters are outlined.
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