Chih‐Jen Lin
Impact in
- Computer Vision and Pattern Recognition top 0.02%
- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 0.01%
- Text and Document Classification Technologies
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Topic Modeling
Papers in
-
- Neural Networks and Applications 39
- Text and Document Classification Technologies 16
- Machine Learning and Data Classification 15
- Stochastic Gradient Optimization Techniques 13
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- Face and Expression Recognition 57
- Co-authors
- Chih-Chung Chang (5 shared papers)Cho‐Jui Hsieh (12 shared papers)Kai‐Wei Chang (10 shared papers)Chih‐Wei Hsu (2 shared papers)Ruby C. Weng (12 shared papers)Wang Xiang-rui (1 shared paper)S. Sathiya Keerthi (6 shared papers)Tingfan Wu (2 shared papers)
- Journals
- Journal of Machine Learning Research (18 papers)Neural Computation (13 papers)ACM Transactions on Intelligent Systems and Technology (4 papers)Machine Learning (3 papers)IEEE Transactions on Neural Networks and Learning Systems (3 papers)
- Partner nations
- TaiwanUnited StatesChina
In The Last Decade
Chih‐Jen Lin
163 papers receiving 56.7k citations
Chih‐Jen Lin's Hit Papers
Peers
Comparison fields: 5 of 234
- Computer Vision and Pattern Recognition 19.0k
- Artificial Intelligence 21.9k
- Signal Processing 5.7k
- Media Technology 4.0k
- Computational Mathematics 248
Countries citing papers authored by Chih‐Jen Lin
This map shows the geographic impact of Chih‐Jen Lin'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 Chih‐Jen Lin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chih‐Jen Lin more than expected).
Fields of papers citing papers by Chih‐Jen Lin
This network shows the impact of papers produced by Chih‐Jen Lin. 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 Chih‐Jen Lin. The network helps show where Chih‐Jen Lin may publish in the future.
Co-authors
The 25 scholars most cited alongside Chih‐Jen Lin, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 169 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | LIBSVM Hit paper breakdown → | 2011 | 29561 |
| 2 | LIBLINEAR: A Library for Large Linear Classification Hit paper breakdown → | 2008 | 5477 |
| 3 | A Practical Guide to Support Vector Classication Hit paper breakdown → | 2008 | 3996 |
| 4 | Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel Hit paper breakdown → | 2003 | 1363 |
| 5 | Projected Gradient Methods for Nonnegative Matrix Factorization Hit paper breakdown → | 2007 | 1297 |
| 6 | Probability Estimates for Multi-class Classification by Pairwise Coupling Hit paper breakdown → | 2004 | 1261 |
| 7 | Working Set Selection Using Second Order Information for Training Support Vector Machines Hit paper breakdown → | 2005 | 1177 |
| 8 | Probability Estimates for Multi-class Classification by Pairwise Coupling Hit paper breakdown → | 2003 | 870 |
| 9 | Combining SVMs with Various Feature Selection Strategies Hit paper breakdown → | 2008 | 816 |
| 10 | Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n ‐peptide compositions Hit paper breakdown → | 2004 | 767 |
| 11 | Load Forecasting Using Support Vector Machines: A Study on EUNITE Competition 2001 Hit paper breakdown → | 2004 | 712 |
| 12 | A note on Platt’s probabilistic outputs for support vector machines Hit paper breakdown → | 2007 | 659 |
| 13 | A dual coordinate descent method for large-scale linear SVM Hit paper breakdown → | 2008 | 652 |
| 14 | Field-aware Factorization Machines for CTR Prediction Hit paper breakdown → | 2016 | 511 |
| 15 | A comparison of methods for multiclass support vector machines Hit paper breakdown → | 2002 | 507 |
| 16 | Parallel Spectral Clustering in Distributed Systems Hit paper breakdown → | 2010 | 486 |
| 17 | 2001 | 421 | |
| 18 | 2005 | 384 | |
| 19 | Training and Testing Low-degree Polynomial Data Mappings via Linear SVM Hit paper breakdown → | 2010 | 376 |
| 20 | 2007 | 311 |
About Chih‐Jen Lin
Chih‐Jen Lin is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Numerical Analysis, Computational Mechanics and Computational Theory and Mathematics, having authored 169 papers that have together received 59.0k indexed citations. Recurring topics across this work include Face and Expression Recognition (57 papers), Neural Networks and Applications (39 papers), Sparse and Compressive Sensing Techniques (23 papers), Advanced Optimization Algorithms Research (22 papers), Text and Document Classification Technologies (16 papers), Machine Learning and Data Classification (15 papers), Stochastic Gradient Optimization Techniques (13 papers) and Recommender Systems and Techniques (12 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (19.0k citations), Artificial Intelligence (21.9k citations), Signal Processing (5.7k citations), Media Technology (4.0k citations) and Computational Mathematics (248 citations). Chih‐Jen Lin has collaborated with scholars based in Taiwan, United States and China. Frequent co-authors include Chih-Chung Chang, Cho‐Jui Hsieh, Kai‐Wei Chang, Chih‐Wei Hsu, Ruby C. Weng, Wang Xiang-rui, S. Sathiya Keerthi, Tingfan Wu, Pai-Hsuen Chen and Yiwei Chen. Their work appears in journals such as Journal of Machine Learning Research, Neural Computation, ACM Transactions on Intelligent Systems and Technology, Machine Learning and IEEE Transactions on Neural Networks and Learning Systems.
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.