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2020 – today
- 2024
[j5]Weite Li, Jiao Pan, Kyoko Hasegawa, Liang Li
, Satoshi Tanaka:
Missing Region Completion Network for Large-Scale Laser-Scanned Point Clouds: Application to Transparent Visualization of Cultural Heritage. Remote. Sens. 16(15): 2758 (2024)- 2022
- [j4]Yongsong Li, Zhengzhou Li, Weite Li, Yuchuan Liu:
Infrared Small Target Detection Based on Gradient-Intensity Joint Saliency Measure. IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens. 15: 7687-7699 (2022) - 2021
- [j3]Weite Li, Kyoko Hasegawa, Liang Li, Akihiro Tsukamoto, Satoshi Tanaka:
Deep Learning-Based Point Upsampling for Edge Enhancement of 3D-Scanned Data and Its Application to Transparent Visualization. Remote. Sens. 13(13): 2526 (2021) - [c13]Weite Li, Kyoko Hasegawa, Liang Li, Akihiro Tsukamoto, Hiroshi Yamaguchi, Fadjar I. Thufail, Brahmantara, Satoshi Tanaka:
Application of the Edge Upsampling Network to Soft-Edge Regions in a 3D-Scanned Point Cloud. AsiaSim 2021: 11-18
2010 – 2019
- 2019
- [j2]Weite Li, Kenya Shigeta, Kyoko Hasegawa, Liang Li, Keiji Yano, Motoaki Adachi, Satoshi Tanaka:
Transparent Collision Visualization of Point Clouds Acquired by Laser Scanning. ISPRS Int. J. Geo Inf. 8(9): 425 (2019) - [c12]Xin Yuan, Weite Li, Kui Lin, Jinglu Hu:
A Deep Neural Network Based Hierarchical Multi-Label Classifier for Protein Function Prediction. CITS 2019: 1-5 - [c11]Yanni Ren, Weite Li, Jinglu Hu:
A Semi-supervised Classification Using Gated Linear Model. IJCNN 2019: 1-7 - 2018
- [c10]Peifeng Liang, Xin Yuan, Weite Li, Jinglu Hu:
A Segmented Local Offset Method for Imbalanced Data Classification Using Quasi-Linear Support Vector Machine. ICPR 2018: 746-751 - [c9]Peifeng Liang, Weite Li, Yudong Wang, Jinglu Hu:
One-Class Classification Using Quasi-Linear Support Vector Machine. SMC 2018: 662-667 - 2017
- [c8]Weite Li, Jinglu Hu:
A multilayer gated bilinear classifier: From optimizing a deep rectified network to a support vector machine. IJCNN 2017: 140-146 - [c7]Peifeng Liang, Weite Li, Donghang Liu, Jinglu Hu:
Large-scale image classification using fast SVM with deep quasi-linear kernel. IJCNN 2017: 1064-1071 - [c6]Weite Li, Peifeng Liang, Xin Yuan, Jinglu Hu:
Non-local information for a mixture of multiple linear classifiers. IJCNN 2017: 3741-3746 - [c5]Weite Li, Benhui Chen, Bo Zhou, Jinglu Hu:
A mixture of multiple linear classifiers with sample weight and manifold regularization. IJCNN 2017: 3747-3752 - 2016
- [j1]Weite Li, Bo Zhou, Benhui Chen, Jinglu Hu:
A Deep Neural Network Based Quasi-Linear Kernel for Support Vector Machines. IEICE Trans. Fundam. Electron. Commun. Comput. Sci. 99-A(12): 2558-2565 (2016) - [c4]Benhui Chen, Weite Li, Yuqing Zhang, Jinglu Hu:
Enhancing multi-label classification based on local label constraints and classifier chains. IJCNN 2016: 1458-1463 - [c3]Weite Li, Jinglu Hu, Benhui Chen:
A deep quasi-linear kernel composition method for support vector machines. IJCNN 2016: 1639-1645 - [c2]Weite Li, Bo Zhou, Jinglu Hu:
A kernel level composition of multiple local classifiers for nonlinear classification. IJCNN 2016: 3845-3850 - 2015
- [c1]Weite Li, Jinglu Hu:
Geometric approach of quasi-linear kernel composition for support vector machine. IJCNN 2015: 1-7
Coauthor Index
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