Kai‐Wei Chang
- Artificial Intelligence top 0.2%
- Computer Vision and Pattern Recognition top 0.5%
- Molecular Biology top 10%
- Information Systems top 1%
- Signal Processing top 1%
- Co-authors
- Cho‐Jui HsiehChih‐Jen LinWang Xiang-ruiS. Sathiya KeerthiS. SundararajanYin-Wen ChangCalvin Yu‐Chian ChenTsung-Ying Tsai
- Topics
- Speech Recognition and Synthesis (8 papers)Face and Expression Recognition (7 papers)Music and Audio Processing (5 papers)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Kai‐Wei Chang
55 papers receiving 6.8k citations
Hit Papers
Peers
Comparison fields: 5 of 211
- Artificial Intelligence 3.4k
- Computer Vision and Pattern Recognition 2.4k
- Molecular Biology 981
- Information Systems 628
- Signal Processing 549
Countries citing papers authored by Kai‐Wei Chang
This map shows the geographic impact of Kai‐Wei Chang'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 Kai‐Wei Chang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kai‐Wei Chang more than expected).
Fields of papers citing papers by Kai‐Wei Chang
This network shows the impact of papers produced by Kai‐Wei Chang. 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 Kai‐Wei Chang. The network helps show where Kai‐Wei Chang may publish in the future.
Co-authorship network of co-authors of Kai‐Wei Chang
This figure shows the co-authorship network connecting the top 25 collaborators of Kai‐Wei Chang. A scholar is included among the top collaborators of Kai‐Wei Chang 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 Kai‐Wei Chang. Kai‐Wei Chang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 9 | |
| 3 | 1 | |
| 4 | 77 | |
| 5 | 13 | |
| 6 | 6 | |
| 7 | 13 | |
| 8 | 9 | |
| 9 | 10 | |
| 10 | 16 | |
| 11 | 39 | |
| 12 | 43 | |
| 13 | 2 | |
| 14 | 107 | |
| 15 | 14 | |
| 16 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVMbreakdown → | 343 |
| 17 | 26 | |
| 18 | An ensemble of three classifiers for KDD cup 2009: expanded linear model, heterogeneous boosting, and selective naïve Bayes | 11 |
| 19 | Coordinate Descent Method for Large-scale L2-loss Linear Support Vector Machines | 162 |
| 20 | LIBLINEAR: A Library for Large Linear Classificationbreakdown → | 4786 |
About Kai‐Wei Chang
Kai‐Wei Chang is a scholar working on Signal Processing, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 55 papers that have together received 7.1k indexed citations. Recurring topics across this work include Speech Recognition and Synthesis (8 papers), Face and Expression Recognition (7 papers) and Music and Audio Processing (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (2.4k citations), Artificial Intelligence (3.4k citations) and Signal Processing (549 citations). Kai‐Wei Chang has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Cho‐Jui Hsieh, Chih‐Jen Lin, Wang Xiang-rui, S. Sathiya Keerthi, S. Sundararajan, Yin-Wen Chang, Calvin Yu‐Chian Chen, Tsung-Ying Tsai, Shau‐Ping Lin and Hong‐Nerng Ho. Their work appears in journals such as Journal of Biological Chemistry, Analytical Chemistry and Scientific Reports.
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.