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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2412.17643 |
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| _version_ | 1866909439250923520 |
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| author | Si, Jing Xu, Jianfei |
| author_facet | Si, Jing Xu, Jianfei |
| contents | The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the current state of research and emerging trends in the field of machine learning in China. Additionally, it identifies the challenges faced in the field of machine learning research and offers suggestions that could serve as valuable references for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_17643 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Advances in Machine Learning Research Using Knowledge Graphs Si, Jing Xu, Jianfei Artificial Intelligence The study uses CSSCI-indexed literature from the China National Knowledge Infrastructure (CNKI) database as the data source. It utilizes the CiteSpace visualization software to draw knowledge graphs on aspects such as institutional collaboration and keyword co-occurrence. This analysis provides insights into the current state of research and emerging trends in the field of machine learning in China. Additionally, it identifies the challenges faced in the field of machine learning research and offers suggestions that could serve as valuable references for future research. |
| title | Advances in Machine Learning Research Using Knowledge Graphs |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2412.17643 |