Open Access
,
volume 36
,
issue 10
,
pages 102266
Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach
Publication type: Journal Article
Publication date: 2024-12-07
scimago Q1
wos Q1
SJR: 1.357
CiteScore: 15.8
Impact factor: 6.1
ISSN: 13191578, 22131248
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GOST
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Zhao Q. et al. Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach // Journal of King Saud University - Computer and Information Sciences. 2024. Vol. 36. No. 10. p. 102266.
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Zhao Q., Zhang M., Li H., Song B., Li Y. Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach // Journal of King Saud University - Computer and Information Sciences. 2024. Vol. 36. No. 10. p. 102266.
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RIS
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TY - JOUR
DO - 10.1016/j.jksuci.2024.102266
UR - https://linkinghub.elsevier.com/retrieve/pii/S1319157824003550
TI - Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach
T2 - Journal of King Saud University - Computer and Information Sciences
AU - Zhao, Qiang
AU - Zhang, Moyan
AU - Li, Hongjuan
AU - Song, Baozhen
AU - Li, Yujun
PY - 2024
DA - 2024/12/07
PB - Springer Nature
SP - 102266
IS - 10
VL - 36
SN - 1319-1578
SN - 2213-1248
ER -
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BibTex (up to 50 authors)
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@article{2024_Zhao,
author = {Qiang Zhao and Moyan Zhang and Hongjuan Li and Baozhen Song and Yujun Li},
title = {Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach},
journal = {Journal of King Saud University - Computer and Information Sciences},
year = {2024},
volume = {36},
publisher = {Springer Nature},
month = {dec},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1319157824003550},
number = {10},
pages = {102266},
doi = {10.1016/j.jksuci.2024.102266}
}
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MLA
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Zhao, Qiang, et al. “Semi-supervised learning for skeleton behavior recognition: A multi-dimensional graph comparison approach.” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 10, Dec. 2024, p. 102266. https://linkinghub.elsevier.com/retrieve/pii/S1319157824003550.