HFS: an intelligent heuristic feature selection scheme to correct uncertainty-Reference-Cited by-同舟云学术

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HFS: an intelligent heuristic feature selection scheme to correct uncertainty

Published:2024-08-22 Issue: Volume: Page:
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Yanli Liu,PengFei Xun,Heng Zhang,Naixue Xiong

Funder

National Natural Science Foundation of China

Shanghai Educational Science Research Project

Humanities and Social Sciences of Ministry of Education Planning Fund, China

Shanghai Science and Technology Program, China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11227-024-06437-7.pdf

Reference44 articles.

1. Zheng S, Wang J, Rizos C, Ding W, El-Mowafy A (2023) Simultaneous localization and mapping (slam) for autonomous driving: concept and analysis. Remote Sens 15(4):1156

2. Cai L, Ye Y, Gao X, Li Z, Zhang C (2021) An improved visual slam based on affine transformation for orb feature extraction. Optik 227:165421

3. Cao Z, Huang Z, Pan L, Zhang S, Liu Z, Fu C (2022) Tctrack: temporal contexts for aerial tracking. arXiv preprint arXiv:2203.01885

4. Michael E, Summers T, Wood TA, Manzie C, Shames I (2022) Probabilistic data association for semantic slam at scale. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 4359–4364. IEEE

5. Zhang H (2022) Deep learning applications in simultaneous localization and mapping. In: Journal of Physics: Conference Series, vol. 2181, IOP Publishing, p 012012

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