Richard Maclin
Impact in
- Artificial Intelligence top 0.5%
- Neural Networks and Applications
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Anomaly Detection Techniques and Applications
- Evolutionary Algorithms and Applications
- Reinforcement Learning in Robotics
- Data Stream Mining Techniques
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- Face and Expression Recognition
Papers in
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- Machine Learning and Data Classification 9
- Neural Networks and Applications 7
- Machine Learning and Algorithms 7
- Evolutionary Algorithms and Applications 6
- Reinforcement Learning in Robotics 5
- Natural Language Processing Techniques 4
- Imbalanced Data Classification Techniques 3
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- Machine Learning in Bioinformatics 3
- Co-authors
- David W. Opitz (3 shared papers)Jude Shavlik (14 shared papers)Kristin P. Bennett (2 shared papers)Ayhan Demiriz (2 shared papers)Trevor Walker (5 shared papers)Lisa Torrey (4 shared papers)Edward W. Wild (2 shared papers)Lars Asker (2 shared papers)
- Journals
- Machine Learning (4 papers)Journal of Artificial Intelligence Research (1 paper)Journal of Aggression Maltreatment & Trauma (1 paper)Conference on Learning Theory (1 paper)Proceedings of International Conference on Neural Networks (ICNN'97) (1 paper)
- Partner nations
- United StatesSwedenPortugal
In The Last Decade
Richard Maclin
27 papers receiving 2.5k citations
Richard Maclin's Hit Papers
Peers
Comparison fields: 5 of 187
- Artificial Intelligence 1.6k
- Computer Vision and Pattern Recognition 519
- Signal Processing 184
- Health Information Management 67
- Information Systems 252
Countries citing papers authored by Richard Maclin
This map shows the geographic impact of Richard Maclin'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 Richard Maclin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Richard Maclin more than expected).
Fields of papers citing papers by Richard Maclin
This network shows the impact of papers produced by Richard Maclin. 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 Richard Maclin. The network helps show where Richard Maclin may publish in the future.
Co-authors
The 21 scholars most cited alongside Richard Maclin, 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 29 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | Popular Ensemble Methods: An Empirical Study Hit paper breakdown → | 1999 | 1947 |
| 2 | An empirical evaluation of bagging and boosting | 1997 | 176 |
| 3 | 2002 | 116 | |
| 4 | Combining the predictions of multiple classifiers: using competitive learning to initialize neural networks | 1995 | 74 |
| 5 | 1996 | 72 | |
| 6 | Giving advice about preferred actions to reinforcement learners via knowledge-based kernel regression | 2005 | 61 |
| 7 | 1993 | 48 | |
| 8 | 2002 | 35 | |
| 9 | A comparative study of support vector machines applied to the supervised word sense disambiguation problem in the medical domain | 2005 | 28 |
| 10 | Incorporating advice into agents that learn from reinforcements | 1994 | 25 |
| 11 | 2021 | 20 | |
| 12 | Ensembles as a sequence of classifiers | 1997 | 17 |
| 13 | Boosting classifiers regionally | 1998 | 13 |
| 14 | 2002 | 12 | |
| 15 | 1997 | 9 | |
| 16 | A simple and effective method for incorporating advice into kernel methods | 2006 | 8 |
| 17 | Refining algorithms with knowledge-based neural networks: improving the Chou-Fasman algorithm for protein folding | 1994 | 7 |
| 18 | Learning from instruction and experience: methods for incorporating procedural domain theories into knowledge-based neural networks | 1996 | 7 |
| 19 | Refining rules incorporated into knowledge-based support vector learners via successive linear programming | 2007 | 6 |
| 20 | 2017 | 5 |
About Richard Maclin
Richard Maclin is a scholar working on Artificial Intelligence, Molecular Biology, Computational Theory and Mathematics, Information Systems and Management Science and Operations Research, having authored 29 papers that have together received 2.7k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (9 papers), Neural Networks and Applications (7 papers), Machine Learning and Algorithms (7 papers), Evolutionary Algorithms and Applications (6 papers), Reinforcement Learning in Robotics (5 papers), Natural Language Processing Techniques (4 papers), Machine Learning in Bioinformatics (3 papers) and Imbalanced Data Classification Techniques (3 papers). The work is most often cited by research in Artificial Intelligence (1.6k citations), Computer Vision and Pattern Recognition (519 citations), Signal Processing (184 citations), Health Information Management (67 citations) and Information Systems (252 citations). Richard Maclin has collaborated with scholars based in United States, Sweden and Portugal. Frequent co-authors include David W. Opitz, Jude Shavlik, Kristin P. Bennett, Ayhan Demiriz, Trevor Walker, Lisa Torrey, Edward W. Wild, Lars Asker, Ted Pedersen and Mahesh Joshi. Their work appears in journals such as Machine Learning, Journal of Artificial Intelligence Research, Journal of Aggression Maltreatment & Trauma, Conference on Learning Theory and Proceedings of International Conference on Neural Networks (ICNN'97).
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.