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检索条件"主题词=multi-output modeling"
9 条 记 录,以下是1-10 订阅
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Gaussian Process Latent Variable Model-Based multi-output modeling of Incomplete Data
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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年 第2期21卷 1941-1951页
作者: Hu, Zhiyong Wang, Chao Wu, Jianguo Du, Dongping Univ Sci & Technol China Dept Precis Machinery & Precis Instrumentat Hefei 230026 Anhui Peoples R China Univ Iowa Dept Ind & Syst Engn Iowa City 52242 IA USA Peking Univ Coll Engn Dept Ind Engn & Management Beijing 100080 Peoples R China Texas Tech Univ Dept Ind Mfg & Syst Engn Lubbock TX 79409 USA
The rapid development of sensor technologies allows the acquisition of high dimensional sensing data. multi-output modeling techniques have been developed to leverage the data for decision making. However, the data of... 详细信息
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Variational Dependent multi-output Gaussian Process Dynamical Systems
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JOURNAL OF MACHINE LEARNING RESEARCH 2016年 第1期17卷 1-36页
作者: Zhao, Jing Sun, Shiliang East China Normal Univ Dept Comp Sci & Technol 500 Dongchuan Rd Shanghai 200241 Peoples R China
This paper presents a dependent multi-output Gaussian process (GP) for modeling complex dynamical systems. The outputs are dependent in this model, which is largely different from previous GP dynamical systems. We ado... 详细信息
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Granular-computing based hybrid collaborative fuzzy clustering for long-term prediction of multiple gas holders levels
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INFORMATION SCIENCES 2016年 330卷 175-185页
作者: Han, Zhongyang Zhao, Jun Liu, Quanli Wang, Wei Dalian Univ Technol Sch Control Sci & Engn Dalian Liaoning Provin Peoples R China
Linz-Donawitz converter Gas (LDG), regarded as an essential secondary energy resource, plays a significant role for the entire production process of steel industry. In a LDG system, the gas holders are crucial equipme... 详细信息
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Autoreplicative random forests with applications to missing value imputation
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MACHINE LEARNING 2024年 第10期113卷 7617-7643页
作者: Antonenko, Ekaterina Carreno, Ander Read, Jesse Ecole Polytech LIX IP Paris F-91120 Palaiseau France PSL Res Univ CBIO Ctr Computat Biol Mines Paris F-75006 Paris France PSL Res Univ Inst Curie F-75005 Paris France INSERM U900 F-75005 Paris France Quant AI Lab Madrid 28043 Spain
Missing values are a common problem in data science and machine learning. Removing instances with missing values is a straightforward workaround, but this can significantly hinder subsequent data analysis, particularl... 详细信息
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Backward Inference in Probabilistic Regressor Chains with Distributional Constraints  22nd
Backward Inference in Probabilistic Regressor Chains with Di...
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22nd International Symposium on Intelligent Data Analysis (IDA)
作者: Antonenko, Ekaterina Mechenich, Michael Beigaite, Rita Zliobaite, Indre Read, Jesse Inst Polytech Paris Ecole Polytech LIX Palaiseau France PSL Res Univ CBIO Ctr Computat Biol Mines Paris F-75006 Paris France PSL Res Univ Inst Curie F-75005 Paris France INSERM U900 F-75005 Paris France Univ Helsinki Helsinki Finland
State-of-the-art approaches for multi-target prediction, such as Regressor Chains, can exploit interdependencies among the targets and model the outputs jointly, by flowing predictions from the first output to the las... 详细信息
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Variational Dependent multi-output Gaussian Process Dynamical Systems  17
Variational Dependent Multi-output Gaussian Process Dynamica...
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17th International Conference on Discovery Science (DS)
作者: Zhao, Jing Sun, Shiliang E China Normal Univ Dept Comp Sci & Technol Shanghai 200241 Peoples R China
This paper presents a dependent multi-output Gaussian process (GP) for modeling complex dynamical systems. The outputs are dependent in this model, which is largely different from previous GP dynamical systems. We ado... 详细信息
来源: 评论
multi-view Collaborative Gaussian Process Dynamical Systems
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JOURNAL OF MACHINE LEARNING RESEARCH 2023年 第1期24卷 1−32-1−32页
作者: Sun, Shiliang Fei, Jingjing Zhao, Jing Mao, Liang East China Normal Univ Sch Comp Sci & Technol Shanghai 200062 Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China
Gaussian process dynamical systems (GPDSs) have shown their effectiveness in many tasks of machine learning. However, when they address multi-view data, current GPDSs do not explicitly model the dependence between pri... 详细信息
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multi-view collaborative Gaussian process dynamical systems
The Journal of Machine Learning Research
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The Journal of Machine Learning Research 2023年 第1期24卷 12066-12097页
作者: Shiliang Sun Jingjing Fei Jing Zhao Liang Mao School of Computer Science and Technology East China Normal University Shanghai P. R. China and Department of Automation Shanghai Jiao Tong University Shanghai P. R. China School of Computer Science and Technology East China Normal University Shanghai P. R. China
Gaussian process dynamical systems (GPDSs) have shown their effectiveness in many tasks of machine learning. However, when they address multi-view data, current GPDSs do not explicitly model the dependence between pri... 详细信息
来源: 评论
Variational dependent multi-output Gaussian process dynamical systems
The Journal of Machine Learning Research
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The Journal of Machine Learning Research 2016年 第1期17卷
作者: Kevin Murphy Bernhard Schölkopf Jing Zhao Shiliang Sun Google MPI for Intelligent Systems Department of Computer Science and Technology East China Normal University Shanghai P. R. China
This paper presents a dependent multi-output Gaussian process (GP) for modeling complex dynamical systems. The outputs are dependent in this model, which is largely different from previous GP dynamical systems. We ado... 详细信息
来源: 评论