Open Access
| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 09027 | |
| Number of page(s) | 8 | |
| Section | Artificial Intelligence and Machine Learning | |
| DOI | https://doi.org/10.1051/epjconf/202429509027 | |
| Published online | 06 May 2024 | |
- M. Ablikim et al. (BESIII), Nucl. Instrum. Meth. A 614, 345 (2010), 0911.4960 [Google Scholar]
- C.A. Zhang, Performance of the BEPC and progress of the BEPCII, in 32nd International Conference on High Energy Physics (2004), pp. 993–997 [Google Scholar]
- D.M. Asner et al., Int. J. Mod. Phys. A 24, S1 (2009), 0809.1869 [Google Scholar]
- M. Ablikim, M.N. Achasov, P. Adlarson, S. Ahmed, M. Albrecht, M. Alekseev, A. Amoroso, F.F. An, Q. An, Y. Bai et al., Chinese Physics C 44, 040001 (2020) [NASA ADS] [CrossRef] [Google Scholar]
- D. Bourilkov, Int. J. Mod. Phys. A 34, 1930019 (2020), 1912.08245 [Google Scholar]
- A.N. Charan, J. Phys. Conf. Ser. 2438, 012111 (2023), 2301.11654 [CrossRef] [Google Scholar]
- V. Khachatryan et al. (CMS), JINST 10, P06005 (2015), 1502.02701 [CrossRef] [Google Scholar]
- L.A. Badulescu, Proceedings of Annals of University of Craiova (2007) [Google Scholar]
- A.J. Myles, R.N. Feudale, Y. Liu, N.A. Woody, S.D. Brown, Journal of Chemometrics: A Journal of the Chemometrics Society 18, 275 (2004) [CrossRef] [Google Scholar]
- J.S. Kushwah, A. Kumar, S. Patel, R. Soni, A. Gawande, S. Gupta, Materials Today: Proceedings 56, 3571 (2022) [CrossRef] [Google Scholar]
- T.H. Lee, A. Ullah, R. Wang, Macroeconomic forecasting in the era of big data: Theory and practice pp. 389–429 (2020) [Google Scholar]
- I.D. Mienye, Y. Sun, Z. Wang, Procedia Manufacturing 35, 698 (2019) [CrossRef] [Google Scholar]
- L. Breiman, Classification and regression trees (Routledge, 2017) [CrossRef] [Google Scholar]
- R.E. Schapire et al., A brief introduction to boosting, in Ijcai (Citeseer, 1999), Vol. 99, pp. 1401–1406 [Google Scholar]
- T. Chen, C. Guestrin, Xgboost: Reliable large-scale tree boosting system, in Proceedings of the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA (2015), pp. 13–17 [Google Scholar]
- S.S. Dhaliwal, A.A. Nahid, R. Abbas, Information 9, 149 (2018) [CrossRef] [Google Scholar]
- W. Chang, Y. Liu, Y. Xiao, X. Yuan, X. Xu, S. Zhang, S. Zhou, Diagnostics 9, 178 (2019) [CrossRef] [PubMed] [Google Scholar]
- T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (2016), pp. 785–794 [Google Scholar]
- W. Li, H. Liu, Z. Deng, K. He, M. He, X. Ji, L. Jiang, H. Li, C. Liu, Q. Ma et al., THE OFFLINE SOFTWARE FOR THE BESIII EXPERIMENT (2006), https://api. semanticscholar.org/CorpusID:221378819 [Google Scholar]
- Y. Shuai, Y. Zheng, H. Huang, Hybrid software obsolescence evaluation model based on PCA-SVM-GridSearchCV, in 2018 IEEE 9th international conference on software engineering and service science (ICSESS) (IEEE, 2018), pp. 449–453 [Google Scholar]
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