Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Lou, Yin"'
Autor:
Wen, Chengyao, Lou, Yin
Rules are widely used in Fintech institutions to make fraud prevention decisions, since rules are highly interpretable thanks to their intuitive if-then structure. In practice, a two-stage framework of fraud prevention decision rule set mining is usu
Externí odkaz:
http://arxiv.org/abs/2311.00964
Publikováno v:
Linchuang shenzangbing zazhi, Vol 24, Iss 7, Pp 550-556 (2024)
Objective To explore the effectiveness and safety of belimumab in Chinese adult lupus nephritis (LN) patients under a real-world setting. Methods For this retrospective cohort study, 30 LN patients received belimumab plus standard of care (SoC) due t
Externí odkaz:
https://doaj.org/article/88de2f2b62c34574a384a82b87998b12
Publikováno v:
Frontiers in Cellular Neuroscience, Vol 18 (2024)
During the development of neural circuits, axons are guided by a variety of molecular cues to navigate through the brain and establish precise connections with correct partners at the right time and place. Many axon guidance cues have been identified
Externí odkaz:
https://doaj.org/article/8d13f5651b5e4d638c32e4eee7590672
Generalized additive models (GAMs) are favored in many regression and binary classification problems because they are able to fit complex, nonlinear functions while still remaining interpretable. In the first part of this paper, we generalize a state
Externí odkaz:
http://arxiv.org/abs/1810.09092
Autor:
Qi, Shi-Chao, Lu, Xiao-Jie, Lou, Yin-Cong, Zhou, Rui, Xue, Ding-Ming, Liu, Xiao-Qin, Sun, Lin-Bing
Publikováno v:
In Engineering September 2022 16:154-161
Black-box risk scoring models permeate our lives, yet are typically proprietary or opaque. We propose Distill-and-Compare, a model distillation and comparison approach to audit such models. To gain insight into black-box models, we treat them as teac
Externí odkaz:
http://arxiv.org/abs/1710.06169
Autor:
Chen, Li, Zhu, Hong-Ming, Li, Yan, Liu, Qi-Fa, Hu, Yu, Zhou, Jian-Feng, Jin, Jie, Hu, Jian-Da, Liu, Ting, Wu, De-Pei, Chen, Jie-Ping, Lai, Yong-Rong, Wang, Jian-Xiang, Li, Juan, Li, Jian-Yong, Du, Xin, Wang, Xin, Yang, Ming-Zhen, Yan, Jin-Song, Ouyang, Gui-Fang, Liu, Li, Hou, Ming, Huang, Xiao-Jun, Yan, Xiao-Jing, Xu, Dan, Li, Wei-Ming, Li, Deng-Ju, Lou, Yin-Jun, Wu, Zheng-Jun, Niu, Ting, Wang, Ying, Li, Xiao-Yang, You, Jian-Hua, Zhao, Hui-Jin, Chen, Yu, Shen, Yang, Chen, Qiu-Sheng, Chen, Yu, Li, Jian, Wang, Bing-Shun, Zhao, Wei-Li, Qing Mi, Jian-, Wang, Kan-Kan, Hu, Jiong, Chen, Zhu, Chen, Sai-Juan, Li, Jun-Min
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Feb 01. 118(6), 1-10.
Externí odkaz:
https://www.jstor.org/stable/27006261
Autor:
Lou, Yin-Cong, Qi, Shi-Chao, Xue, Ding-Ming, Gu, Chen, Zhou, Rui, Liu, Xiao-Qin, Sun, Lin-Bing
Publikováno v:
In Chemical Engineering Journal 1 November 2020 399
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The generalized partially linear additive model (GPLAM) is a flexible and interpretable approach to building predictive models. It combines features in an additive manner, allowing each to have either a linear or nonlinear effect on the response. How
Externí odkaz:
http://arxiv.org/abs/1407.4729