John Platt
Impact in
- Computer Vision and Pattern Recognition top 0.05%
- Face and Expression Recognition
- Advanced Image and Video Retrieval Techniques
- Image Retrieval and Classification Techniques
- Artificial Intelligence top 0.01%
- Anomaly Detection Techniques and Applications
- Neural Networks and Applications
- Text and Document Classification Technologies
Papers in
-
- Neural Networks and Applications 21
- Machine Learning and Algorithms 15
- Machine Learning and Data Classification 9
- Text and Document Classification Technologies 9
- Gaussian Processes and Bayesian Inference 8
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- Face and Expression Recognition 19
- Co-authors
- Bernhard Schölkopf (58 shared papers)John Shawe‐Taylor (3 shared papers)Susan Dumais (2 shared papers)Robert C. Williamson (4 shared papers)Alex Smola (2 shared papers)Marti A. Hearst (1 shared paper)E. Osuna (1 shared paper)Alan H. Barr (7 shared papers)
- Journals
- Science (3 papers)Neural Computation (2 papers)Environmental Research Communications (2 papers)Age and Ageing (1 paper)IEEE Signal Processing Letters (1 paper)
- Partner nations
- United StatesUnited KingdomAustralia
In The Last Decade
John Platt
177 papers receiving 33.3k citations
John Platt's Hit Papers
Peers
Comparison fields: 5 of 237
- Computer Vision and Pattern Recognition 11.2k
- Artificial Intelligence 16.8k
- Computer Graphics and Computer-Aided Design 1.6k
- Signal Processing 4.1k
- Media Technology 1.5k
Countries citing papers authored by John Platt
This map shows the geographic impact of John Platt'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 Platt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John Platt more than expected).
Fields of papers citing papers by John Platt
This network shows the impact of papers produced by John Platt. 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 Platt. The network helps show where John Platt may publish in the future.
Co-authors
The 25 scholars most cited alongside John Platt, 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 182 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Support vector machines Hit paper breakdown → | 1998 | 5085 |
| 2 | Fast Training of Support Vector Machines Using Sequential Minimal Optimization Hit paper breakdown → | 1998 | 4388 |
| 3 | Estimating the Support of a High-Dimensional Distribution Hit paper breakdown → | 2001 | 4371 |
| 4 | Probabilistic Outputs for Support vector Machines and Comparisons to Regularized Likelihood Methods Hit paper breakdown → | 1999 | 3644 |
| 5 | Sequential Minimal Optimization : A Fast Algorithm for Training Support Vector Machines Hit paper breakdown → | 1998 | 2126 |
| 6 | Best practices for convolutional neural networks applied to visual document analysis Hit paper breakdown → | 2005 | 1991 |
| 7 | Support Vector Method for Novelty Detection Hit paper breakdown → | 1999 | 1546 |
| 8 | Large Margin DAG's for Multiclass Classification Hit paper breakdown → | 1999 | 1392 |
| 9 | Elastically deformable models Hit paper breakdown → | 1987 | 1385 |
| 10 | Inductive learning algorithms and representations for text categorization Hit paper breakdown → | 1998 | 1173 |
| 11 | A Resource-Allocating Network for Function Interpolation Hit paper breakdown → | 1991 | 1101 |
| 12 | Elastically deformable models Hit paper breakdown → | 1987 | 910 |
| 13 | From captions to visual concepts and back Hit paper breakdown → | 2015 | 874 |
| 14 | Multiple Instance Boosting for Object Detection Hit paper breakdown → | 2005 | 526 |
| 15 | Social traps. Hit paper breakdown → | 1973 | 388 |
| 16 | 2004 | 255 | |
| 17 | 1988 | 249 | |
| 18 | 2005 | 239 | |
| 19 | Using Analytic QP and Sparseness to Speed Training of Support Vector Machines | 1998 | 227 |
| 20 | Proceedings of the 20th International Conference on Neural Information Processing Systems | 2007 | 223 |
About John Platt
John Platt is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing, Computer Networks and Communications and Control and Systems Engineering, having authored 182 papers that have together received 35.8k indexed citations. Recurring topics across this work include Music and Audio Processing (24 papers), Neural Networks and Applications (21 papers), Face and Expression Recognition (19 papers), Machine Learning and Algorithms (15 papers), Speech and Audio Processing (15 papers), Machine Learning and Data Classification (9 papers), Text and Document Classification Technologies (9 papers) and Gaussian Processes and Bayesian Inference (8 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (11.2k citations), Artificial Intelligence (16.8k citations), Computer Graphics and Computer-Aided Design (1.6k citations), Signal Processing (4.1k citations) and Media Technology (1.5k citations). John Platt has collaborated with scholars based in United States, United Kingdom and Australia. Frequent co-authors include Bernhard Schölkopf, John Shawe‐Taylor, Susan Dumais, Robert C. Williamson, Alex Smola, Marti A. Hearst, E. Osuna, Alan H. Barr, Don Steinkraus and P. Simard. Their work appears in journals such as Science, Neural Computation, Environmental Research Communications, Age and Ageing and IEEE Signal Processing Letters.
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.