Zobrazeno 1 - 10
of 453
pro vyhledávání: '"Liu, Li-Ming"'
Autor:
Chen, Lei1 (AUTHOR), Liu, Li-Ming2 (AUTHOR), Guo, Mei1 (AUTHOR), Du, Yang1 (AUTHOR), Chen, Yue-wen3,4 (AUTHOR), Xiong, Xi-Yue5 (AUTHOR) xiong_fybj@163.com, Cheng, Yong1,2,5 (AUTHOR) yongcheng@muc.edu.cn
Publikováno v:
BMC Psychiatry. 7/1/2024, Vol. 24 Issue 1, p1-9. 9p.
Autor:
Cao, Zhen-Dong, Zhang, Peng, Zhang, Si-Hang, Yang, Yi-Hong, Chen, Jia-Yi, Liu, Li-Ming, Li, Xiang-Chuan, Xie, San-Ping
Publikováno v:
In Palaeoworld March 2024 33(1):216-228
Autor:
Liu, De-Jian, Xu, Ye, Li, Ying-Jie, Zheng, Sheng, Lu, Deng-Rong, Hao, Chao-Jie, Lin, Ze-Hao, Bian, Shuai-Bo, Liu, Li-Ming
We present a study of molecular outflows using six molecular lines (including 12CO/13CO/C18O/HCO+(J = 1-0) and SiO/CS(J = 2-1)) toward nine nearby high-mass star-forming regions with accurate known distances. This work is based on the high-sensitivit
Externí odkaz:
http://arxiv.org/abs/2012.03226
Autor:
Deng, Qing-Xue, Jia, Xin-Hong, Liu, Li-Ming, Tang, Yu-Quan, Jiang, Li, Song, Wei-Jie, Zou, Mei-Ling, Deng, Sha-Sha, Wang, Qing-Yi
Publikováno v:
In Optical Fiber Technology December 2023 81
Classical principal component analysis (PCA) may suffer from the sensitivity to outliers and noise. Therefore PCA based on $\ell_1$-norm and $\ell_p$-norm ($0 < p < 1$) have been studied. Among them, the ones based on $\ell_p$-norm seem to be most in
Externí odkaz:
http://arxiv.org/abs/2005.12263
Akademický článek
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Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems, 2020
In this paper, we propose a general model for plane-based clustering. The general model contains many existing plane-based clustering methods, e.g., k-plane clustering (kPC), proximal plane clustering (PPC), twin support vector clustering (TWSVC) and
Externí odkaz:
http://arxiv.org/abs/1901.09178
Akademický článek
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Publikováno v:
Information Sciences, Volume 462, September 2018, Pages 114-131
Stochastic gradient descent algorithm has been successfully applied on support vector machines (called PEGASOS) for many classification problems. In this paper, stochastic gradient descent algorithm is investigated to twin support vector machines for
Externí odkaz:
http://arxiv.org/abs/1704.05596
Publikováno v:
Nanomaterials (2079-4991); Aug2024, Vol. 14 Issue 15, p1251, 11p