1 code implementation • 16 Aug 2019 • Zhenhua Shi, Xiaomo Chen, Changming Zhao, He He, Veit Stuphorn, Dongrui Wu
Multi-view learning improves the learning performance by utilizing multi-view data: data collected from multiple sources, or feature sets extracted from the same data source.
1 code implementation • 9 Jan 2020 • Zhenhua Shi, Dongrui Wu, Jian Huang, Yu-Kai Wang, Chin-Teng Lin
Approaches that preserve only the local data structure, such as locality preserving projections, are usually unsupervised (and hence cannot use label information) and uses a fixed similarity graph.
1 code implementation • 21 Mar 2020 • Changming Zhao, Dongrui Wu, Jian Huang, Ye Yuan, Hai-Tao Zhang, Ruimin Peng, Zhenhua Shi
Bootstrap aggregating (Bagging) and boosting are two popular ensemble learning approaches, which combine multiple base learners to generate a composite model for more accurate and more reliable performance.
2 code implementations • 30 Nov 2020 • Zhenhua Shi, Dongrui Wu, Chenfeng Guo, Changming Zhao, Yuqi Cui, Fei-Yue Wang
To effectively optimize Takagi-Sugeno-Kang (TSK) fuzzy systems for regression problems, a mini-batch gradient descent with regularization, DropRule, and AdaBound (MBGD-RDA) algorithm was recently proposed.