Open Access
| Issue |
EPJ Web Conf.
Volume 337, 2025
27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)
|
|
|---|---|---|
| Article Number | 01194 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/epjconf/202533701194 | |
| Published online | 07 October 2025 | |
- V. Klochkov, The compressed baryonic matter experiment at fair, Nuclear Physics A 1005, 121945 (2021), the 28th International Conference on Ultra-relativistic Nucleus-Nucleus Collisions: Quark Matter 2019. https://doi.org/10.1016/j.nuclphysa.2020.121945 [Google Scholar]
- V. Friese, for the CBM Collaboration, The high-rate data challenge: computing for the cbm experiment, Journal of Physics: Conference Series 898, 112003 (2017). 10.1088/1742-6596/898/11/112003 [Google Scholar]
- M. Bleicher et al., Relativistic hadron hadron collisions in the ultrarelativistic quantum molecular dynamics model, J. Phys. G 25, 1859 (1999), hep-ph/9909407. 10.1088/0954-3899/25/9/308 [CrossRef] [Google Scholar]
- I. Frohlich, T. Galatyuk, R. Holzmann, J. Markert, B. Ramstein, P. Salabura, J. Stroth, Design of the Pluto Event Generator, J. Phys. Conf. Ser. 219, 032039 (2010), 0905.2568. 10.1088/1742-6596/219/3/032039 [Google Scholar]
- L. Grinsztajn, E. Oyallon, G. Varoquaux, Why do tree-based models still outperform deep learning on typical tabular data?, in Advances in Neural Information Processing Systems, edited by S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh (Curran Associates, Inc., 2022), Vol. 35, pp. 507–520, https://proceedings.neurips.cc/paper_files/paper/2022/file/0378c7692da36807bdec87ab043cdadc-Paper-Datasets_and_Benchmarks.pdf [Google Scholar]
- Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting, Journal of Computer and System Sciences 55, 119 (1997). https://doi.org/10.1006/jcss.1997.1504 [Google Scholar]
- Y. Lou, M. Obukhov, BDT: Gradient Boosted Decision Tables for High Accuracy and Scoring Efficiency, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Association for Computing Machinery, New York, NY, USA, 2017), KDD ’17, p. 1893–1901, ISBN 9781450348874, https://doi.org/10.1145/3097983.3098175 [Google Scholar]
- T. Chen, C. Guestrin, Xgboost: A scalable tree boosting system, CoRR abs/1603.02754 (2016), 1603.02754. [Google Scholar]
- P. Englert, Improved Precision in Vh( bb¯) via Boosted Decision Trees (2024), 2407.21239. [Google Scholar]
- Y. Rana, A.K. Dubey, An Analytical Comparison Among Various Multivariate Methods Used for Particle Discrimination, Springer Proc. Phys. 304, 974 (2024). 10.1007/978-981-97-0289-3_258 [Google Scholar]
- Coadou, Yann, Boosted decision trees and applications, EPJ Web of Conferences 55, 02004 (2013). 10.1051/epjconf/20135502004 [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
- 228172, Tech. Rep. CBM Progress Report 2019, Darmstadt (2020), https://repository.gsi.de/record/228172 [Google Scholar]
- P. Senger, V. Friese (CBM Collaboration), Tech. Rep. CBM Progress Report 2021, Darmstadt (2022), https://repository.gsi.de/record/246663 [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.