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307 results on '"Wang, Tinghua"'

3. A Model Predictive Control Approach for Reconfigurable Battery Energy Storage Systems

Author

Yan, Bo, Wang, Tinghua, Wu, Jingyun, He, Darui, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Tan, Kay Chen, Series Editor, Yang, Qingxin, editor, Li, Zewen, editor, and Luo, An, editor

Published
2024
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11. A Feature-Weighted Support Vector Regression Machine Based on Hilbert–Schmidt Independence Criterion Least Absolute Shrinkage and Selection Operator.

Author

Zhang, Xin, Wang, Tinghua, and Lai, Zhiyong

Subjects
*SUPPORT vector machines, *KERNEL functions, *MACHINE learning, *FORECASTING, *ALGORITHMS
Abstract

Support vector regression (SVR) is a powerful kernel-based regression prediction algorithm that performs excellently in various application scenarios. However, for real-world data, the general SVR often fails to achieve good predictive performance due to its inability to assess feature contribution accurately. Feature weighting is a suitable solution to address this issue, applying correlation measurement methods to obtain reasonable weights for features based on their contributions to the output. In this paper, based on the idea of a Hilbert–Schmidt independence criterion least absolute shrinkage and selection operator (HSIC LASSO) for selecting features with minimal redundancy and maximum relevance, we propose a novel feature-weighted SVR that considers the importance of features to the output and the redundancy between features. In this approach, the HSIC is utilized to effectively measure the correlation between features as well as that between features and the output. The feature weights are obtained by solving a LASSO regression problem. Compared to other feature weighting methods, our method takes much more comprehensive consideration of weight calculation, and the obtained weighted kernel function can lead to more precise predictions for unknown data. Comprehensive experiments on real datasets from the University of California Irvine (UCI) machine learning repository demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Published
2024
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21. Multiple Kernel Fusion with HSIC Lasso

Author

Wang, Tinghua, Liu, Fulai, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Geng, Xin, editor, and Kang, Byeong-Ho, editor

Published
2018
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25. Kernel Learning with Hilbert-Schmidt Independence Criterion

Author

Wang, Tinghua, Li, Wei, He, Xianwen, Diniz Junqueira Barbosa, Simone, Series editor, Chen, Phoebe, Series editor, Du, Xiaoyong, Series editor, Filipe, Joaquim, Series editor, Kara, Orhun, Series editor, Kotenko, Igor, Series editor, Liu, Ting, Series editor, Sivalingam, Krishna M., Series editor, Washio, Takashi, Series editor, Tan, Tieniu, editor, Li, Xuelong, editor, Chen, Xilin, editor, Zhou, Jie, editor, Yang, Jian, editor, and Cheng, Hong, editor

Published
2016
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26. Learning Class-Informed Semantic Similarity

Author

Wang, Tinghua, Li, Wei, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Hirose, Akira, editor, Ozawa, Seiichi, editor, Doya, Kenji, editor, Ikeda, Kazushi, editor, Lee, Minho, editor, and Liu, Derong, editor

Published
2016
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30. Hybrid-Strategy Improved Golden Jackal Optimization.

Author

ZHU Xinglin, WANG Tinghua, and LAI Zhiyong

Subjects
PARTICLE swarm optimization, SEARCH algorithms, ALGORITHMS
Abstract

In view of the shortcomings of the golden jackal optimization (GJO) in solving complex optimization problems, such as slow convergence speed and being easy to fall into local optimum, a hybrid-strategy improved golden jackal optimization (IGJO) is proposed. Firstly, when the optimal solution of the algorithm stagnates updating, the Cauchy variation strategy is introduced to enhance the population diversity and improve the global search capability of the algorithm to avoid falling into local optimum. Then, a decision strategy based on weight is proposed to accelerate the convergence of the algorithm by assigning different weights to golden jackal individuals. Experiments with eight benchmark functions and some CEC2017 test functions show that the improved algorithm has better optimization performance and convergence speed. Furthermore, the improved algorithm is applied to optimize the parameters of support vector regression (SVR) model, and its effectiveness is verified by experiments on 5 UCI (University of California, Irvine) datasets. [ABSTRACT FROM AUTHOR]

Published
2024
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33. Hydrogel encapsulated stem cells facilitate successful repair after spinal cord injury in rats and monkeys

Author

Li, Tianqing, primary, Li, Pengfei, additional, Yuan, Hao, additional, chen, Yanyin, additional, Zhu, Xiaoqing, additional, Xiong, Liulin, additional, Zhao, Shumei, additional, Li, Jun, additional, Chen, Tingwei, additional, Ai, Zongyong, additional, Cai, Hongzhi, additional, Feng, Chun, additional, Li, Yong, additional, Wang, Jiaojian, additional, Niu, Yongming, additional, Liu, Jinxiang, additional, Ji, Weizhi, additional, Zhang, Lei, additional, and Wang, Tinghua, additional

Published
2023
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38. Using Exponential Kernel for Word Sense Disambiguation

Author

Wang, Tinghua, Rao, Junyang, Zhao, Dongyan, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Mladenov, Valeri, editor, Koprinkova-Hristova, Petia, editor, Palm, Günther, editor, Villa, Alessandro E. P., editor, Appollini, Bruno, editor, and Kasabov, Nikola, editor

Published
2013
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