Leonard E. Trigg
Impact in
- Software top 5%
- Software Reliability and Analysis Research
- Software Testing and Debugging Techniques
- Artificial Intelligence top 5%
- Imbalanced Data Classification Techniques
- Natural Language Processing Techniques
- Topic Modeling
Papers in
-
- Machine Learning and Data Classification 3
- Bayesian Modeling and Causal Inference 2
- Neural Networks and Applications 1
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- Data Mining Algorithms and Applications 3
- Co-authors
- Geoffrey Holmes (3 shared papers)Ian H. Witten (3 shared papers)Eibe Frank (3 shared papers)Mark A. Hall (1 shared paper)Sally Jo Cunningham (1 shared paper)Sean A. Irvine (1 shared paper)Stuart J. Inglis (1 shared paper)John G. Cleary (1 shared paper)
- Journals
- Machine Learning (1 paper)Research Commons (The University of Waikato) (1 paper)Research Commons (University of Waikato) (3 papers)
- Partner nations
- New Zealand
In The Last Decade
Leonard E. Trigg
5 papers receiving 627 citations
Peers
Comparison fields: 5 of 115
- Software 74
- Artificial Intelligence 315
- Information Systems 193
- Signal Processing 87
- Health Information Management 34
Countries citing papers authored by Leonard E. Trigg
This map shows the geographic impact of Leonard E. Trigg'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 Leonard E. Trigg with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Leonard E. Trigg more than expected).
Fields of papers citing papers by Leonard E. Trigg
This network shows the impact of papers produced by Leonard E. Trigg. 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 Leonard E. Trigg. The network helps show where Leonard E. Trigg may publish in the future.
Co-authors
The 9 scholars most cited alongside Leonard E. Trigg, 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 | Weka: Practical machine learning tools and techniques with Java implementations | 1999 | 421 |
| 2 | 2000 | 161 | |
| 3 | 1998 | 51 | |
| 4 | 2007 | 48 | |
| 5 | 1998 | 2 |
About Leonard E. Trigg
Leonard E. Trigg is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Software and Infectious Diseases, having authored 5 papers that have together received 683 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (3 papers), Data Mining Algorithms and Applications (3 papers), Bayesian Modeling and Causal Inference (2 papers), Time Series Analysis and Forecasting (1 paper), Advanced Malware Detection Techniques (1 paper), Software Testing and Debugging Techniques (1 paper), Neural Networks and Applications (1 paper) and Software Reliability and Analysis Research (1 paper). The work is most often cited by research in Software (74 citations), Artificial Intelligence (315 citations), Information Systems (193 citations), Signal Processing (87 citations) and Health Information Management (34 citations). Leonard E. Trigg has collaborated with scholars based in New Zealand. Frequent co-authors include Geoffrey Holmes, Ian H. Witten, Eibe Frank, Mark A. Hall, Sally Jo Cunningham, Sean A. Irvine, Stuart J. Inglis, John G. Cleary and Mark Utting. Their work appears in journals such as Machine Learning, Research Commons (The University of Waikato) and Research Commons (University of Waikato).
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.