John Shawe‐Taylor
Impact in
- Artificial Intelligence top 0.01%
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Text and Document Classification Technologies
- Machine Learning and Algorithms
- Computer Vision and Pattern Recognition top 0.05%
- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
Papers in
-
- Machine Learning and Algorithms 58
- Neural Networks and Applications 52
- Machine Learning and Data Classification 22
- Text and Document Classification Technologies 21
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- Face and Expression Recognition 53
- Image Retrieval and Classification Techniques 20
- Co-authors
- Nello Cristianini (28 shared papers)John Platt (3 shared papers)Robert C. Williamson (8 shared papers)Bernhard Schölkopf (4 shared papers)Alex Smola (3 shared papers)David R. Hardoon (25 shared papers)Sándor Szedmák (13 shared papers)F. J. R. Taylor (1 shared paper)
- Journals
- Journal of Machine Learning Research (9 papers)Discrete Applied Mathematics (7 papers)Machine Learning (7 papers)IEEE Transactions on Information Theory (7 papers)Neurocomputing (5 papers)
- Partner nations
- United KingdomUnited StatesAustralia
In The Last Decade
John Shawe‐Taylor
305 papers receiving 34.5k citations
John Shawe‐Taylor's Hit Papers
Peers
Comparison fields: 5 of 229
- Artificial Intelligence 16.2k
- Computer Vision and Pattern Recognition 10.2k
- Signal Processing 3.6k
- Media Technology 1.8k
- Computational Mathematics 120
Countries citing papers authored by John Shawe‐Taylor
This map shows the geographic impact of John Shawe‐Taylor'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 John Shawe‐Taylor with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Shawe‐Taylor more than expected).
Fields of papers citing papers by John Shawe‐Taylor
This network shows the impact of papers produced by John Shawe‐Taylor. 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 John Shawe‐Taylor. The network helps show where John Shawe‐Taylor may publish in the future.
Co-authors
The 25 scholars most cited alongside John Shawe‐Taylor, 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 313 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | An Introduction to Support Vector Machines and Other Kernel-based Learning Methods Hit paper breakdown → | 2000 | 9790 |
| 2 | Kernel Methods for Pattern Analysis Hit paper breakdown → | 2004 | 4168 |
| 3 | Estimating the Support of a High-Dimensional Distribution Hit paper breakdown → | 2001 | 3840 |
| 4 | An Introduction to Support Vector Machines Hit paper breakdown → | 2000 | 2860 |
| 5 | Amplification and direct sequencing of fungal ribosomal RNA genes for phylogenetics. PCR protocols: a guide to methods and applications Hit paper breakdown → | 1990 | 2373 |
| 6 | Canonical Correlation Analysis: An Overview with Application to Learning Methods Hit paper breakdown → | 2004 | 2235 |
| 7 | Support Vector Method for Novelty Detection Hit paper breakdown → | 1999 | 1400 |
| 8 | Large Margin DAG's for Multiclass Classification Hit paper breakdown → | 1999 | 1191 |
| 9 | Proceedings of the 24th International Conference on Neural Information Processing Systems Hit paper breakdown → | 2011 | 814 |
| 10 | Text Classification using String Kernels | 2000 | 377 |
| 11 | 1998 | 321 | |
| 12 | 2002 | 268 | |
| 13 | Two view learning: SVM-2K, Theory and Practice | 2005 | 246 |
| 14 | 2011 | 184 | |
| 15 | 2010 | 176 | |
| 16 | 2017 | 168 | |
| 17 | Kernel-Based Learning of Hierarchical Multilabel Classification Models | 2006 | 160 |
| 18 | Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis | 2002 | 150 |
| 19 | 2002 | 146 | |
| 20 | 2014 | 142 |
About John Shawe‐Taylor
John Shawe‐Taylor is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computational Theory and Mathematics and Computational Mechanics, having authored 313 papers that have together received 36.9k indexed citations. Recurring topics across this work include Machine Learning and Algorithms (58 papers), Face and Expression Recognition (53 papers), Neural Networks and Applications (52 papers), Sparse and Compressive Sensing Techniques (23 papers), Machine Learning and Data Classification (22 papers), Text and Document Classification Technologies (21 papers), Image Retrieval and Classification Techniques (20 papers) and Blind Source Separation Techniques (19 papers). The work is most often cited by research in Artificial Intelligence (16.2k citations), Computer Vision and Pattern Recognition (10.2k citations), Signal Processing (3.6k citations), Media Technology (1.8k citations) and Computational Mathematics (120 citations). John Shawe‐Taylor has collaborated with scholars based in United Kingdom, United States and Australia. Frequent co-authors include Nello Cristianini, John Platt, Robert C. Williamson, Bernhard Schölkopf, Alex Smola, David R. Hardoon, Sándor Szedmák, F. J. R. Taylor, D. Lee Taylor and Suk‐Ha Lee. Their work appears in journals such as Journal of Machine Learning Research, Discrete Applied Mathematics, Machine Learning, IEEE Transactions on Information Theory and Neurocomputing.
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.