I C G Campbell
- Artificial Intelligence top 5%
- Machine Learning and Algorithms 2
- Gaussian Processes and Bayesian Inference 2
- Neural Networks and Applications 1
- Evolutionary Algorithms and Applications 1
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- Generative Adversarial Networks and Image Synthesis 1
- Health Information Management top 10%
- Artificial Intelligence in Healthcare 1
- Signal Processing top 10%
- Media Technology top 10%
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- Engineering Applied Research 1
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- Gear and Bearing Dynamics Analysis 1
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionHealth Information Management
- Journals
- Bristol Research (University of Bristol) (5 papers)UCL Discovery (University College London) (1 paper)
In The Last Decade
I C G Campbell
10 papers receiving 620 citations
Peers
Comparison fields: 5 of 111
- Artificial Intelligence 427
- Computer Vision and Pattern Recognition 182
- Health Information Management 24
- Signal Processing 53
- Media Technology 30
Countries citing papers authored by I C G Campbell
This map shows the geographic impact of I C G Campbell'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 I C G Campbell with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites I C G Campbell more than expected).
Fields of papers citing papers by I C G Campbell
This network shows the impact of papers produced by I C G Campbell. 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 I C G Campbell. The network helps show where I C G Campbell may publish in the future.
Co-authorship network
The 6 scholars most cited alongside I C G Campbell, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Robust Bayes Point Machines | 2000 | 7 |
| 2 | Radial Basis Function Networks: Design and Applications | 2000 | 16 |
| 3 | Proc.17th Int Conference on Machine Learning | 2000 | 9 |
| 4 | Bayes Point Machines: Estimating the Bayes Point in Kernel Space | 1999 | 25 |
| 5 | Controlling the Sensitivity of Support Vector Machines | 1999 | 487 |
| 6 | Multiplicative Updatings for Support Vector Machines | 1999 | 3 |
| 7 | Bayesian Learning in Reproducing Kernel Hilbert Spaces: The Usefulness of the Bayes Point | 1999 | 3 |
| 8 | The Application of Support Vector Machines to Medical decision Support: A Case Study | 1999 | 30 |
| 9 | The Kernel-Adatron : A fast and simple learning procedure for support vector machines | 1998 | 61 |
| 10 | European Symposium on Artificial Neural Networks ESANN '95 | 1995 | 26 |
About I C G Campbell
I C G Campbell is a scholar working on Health Information Management, Artificial Intelligence and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 667 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (2 papers), Gaussian Processes and Bayesian Inference (2 papers), Artificial Intelligence in Healthcare (1 paper), Engineering Applied Research (1 paper), Neural Networks and Applications (1 paper), Gear and Bearing Dynamics Analysis (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper) and Evolutionary Algorithms and Applications (1 paper). The work is most often cited by research in Artificial Intelligence (427 citations), Computer Vision and Pattern Recognition (182 citations) and Health Information Management (24 citations). Frequent co-authors include Nello Cristianini, K Veropoulos, Consuelo Vicente, Ralf Herbrich, Alex Smola and John Shawe‐Taylor. Their work appears in journals such as Bristol Research (University of Bristol) and UCL Discovery (University College London).
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.