Chia-Hua Ho
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Computational Mechanics top 10%
- Information Systems top 10%
- Molecular Biology
- Co-authors
- Chih‐Jen LinGuo-Xun YuanShou-De LinChe‐Wei ChangTsung-Ting KuoYu-Shi LinHsuan-Tien LinHsun-Ping Hsieh
- Topics
- Face and Expression Recognition (3 papers)Machine Learning and Data Classification (3 papers)Sparse and Compressive Sensing Techniques (2 papers)
- Journals
- Proceedings of the IEEEJournal of Machine Learning ResearchKnowledge Discovery and Data Mining
- Partner nations
- Taiwan
In The Last Decade
Chia-Hua Ho
8 papers receiving 543 citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 315
- Computer Vision and Pattern Recognition 180
- Computational Mechanics 85
- Information Systems 50
- Molecular Biology 46
Countries citing papers authored by Chia-Hua Ho
This map shows the geographic impact of Chia-Hua Ho's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Chia-Hua Ho with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chia-Hua Ho more than expected).
Fields of papers citing papers by Chia-Hua Ho
This network shows the impact of papers produced by Chia-Hua Ho. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Chia-Hua Ho. The network helps show where Chia-Hua Ho may publish in the future.
Co-authorship network of co-authors of Chia-Hua Ho
This figure shows the co-authorship network connecting the top 25 collaborators of Chia-Hua Ho. A scholar is included among the top collaborators of Chia-Hua Ho based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Chia-Hua Ho. Chia-Hua Ho is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 18 | |
| 2 | An improved GLMNET for L1-regularized logistic regression | 80 |
| 3 | Large-scale linear support vector regression | 114 |
| 4 | A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012 | 18 |
| 5 | 181 | |
| 6 | Active Learning and Experimental Design with SVMs | 6 |
| 7 | 66 | |
| 8 | Feature Engineering and Classifier Ensemble for KDD Cup 2010 | 89 |
About Chia-Hua Ho
Chia-Hua Ho is a scholar working on Computer Science Applications, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 8 papers that have together received 572 indexed citations. Recurring topics across this work include Face and Expression Recognition (3 papers), Machine Learning and Data Classification (3 papers) and Sparse and Compressive Sensing Techniques (2 papers). The work is most often cited by research in Computational Mathematics (7 citations), Artificial Intelligence (315 citations) and Computer Vision and Pattern Recognition (180 citations). Chia-Hua Ho has collaborated with scholars based in Taiwan. Frequent co-authors include Chih‐Jen Lin, Guo-Xun Yuan, Shou-De Lin, Che‐Wei Chang, Tsung-Ting Kuo, Yu-Shi Lin, Hsuan-Tien Lin, Hsun-Ping Hsieh, Hsiang‐Fu Yu and Hung-Yi Lo. Their work appears in journals such as Proceedings of the IEEE, Journal of Machine Learning Research and Knowledge Discovery and Data Mining.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.