Leonard E. Trigg
Impact in
- Software top 5%
- Software Reliability and Analysis Research
- Artificial Intelligence top 2%
- Machine Learning and Data Classification
- Imbalanced Data Classification Techniques
- Topic Modeling
- Natural Language Processing Techniques
Papers in
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- Machine Learning and Data Classification 5
- Bayesian Modeling and Causal Inference 3
- Machine Learning and Algorithms 2
- Evolutionary Algorithms and Applications 1
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- Data Mining Algorithms and Applications 3
- Co-authors
- John G. Cleary (3 shared papers)Geoffrey Holmes (4 shared papers)Eibe Frank (3 shared papers)Ian H. Witten (3 shared papers)Sally Jo Cunningham (1 shared paper)Mark A. Hall (1 shared paper)Sean A. Irvine (1 shared paper)Stuart J. Inglis (1 shared paper)
- Journals
- Machine Learning (1 paper)Research Commons (University of Waikato) (3 papers)Queensland's institutional digital repository (The University of Queensland) (1 paper)Research Commons (The University of Waikato) (1 paper)Elsevier eBooks (1 paper)
- Partner nations
- New ZealandUnited Kingdom
In The Last Decade
Leonard E. Trigg
7 papers receiving 1.3k citations
Leonard E. Trigg's Hit Papers
Peers
Comparison fields: 5 of 144
- Software 112
- Artificial Intelligence 690
- Health Information Management 73
- Information Systems 378
- Signal Processing 170
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 10 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 | K*: An Instance-based Learner Using an Entropic Distance Measure Hit paper breakdown → | 1995 | 604 |
| 2 | Weka: Practical machine learning tools and techniques with Java implementations Hit paper breakdown → | 1999 | 503 |
| 3 | 2000 | 198 | |
| 4 | 1998 | 64 | |
| 5 | 2007 | 55 | |
| 6 | 2001 | 3 | |
| 7 | 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 7 papers that have together received 1.4k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (5 papers), Bayesian Modeling and Causal Inference (3 papers), Data Mining Algorithms and Applications (3 papers), Machine Learning and Algorithms (2 papers), Evolutionary Algorithms and Applications (1 paper), Software Reliability and Analysis Research (1 paper), Software Testing and Debugging Techniques (1 paper) and Time Series Analysis and Forecasting (1 paper). The work is most often cited by research in Software (112 citations), Artificial Intelligence (690 citations), Health Information Management (73 citations), Information Systems (378 citations) and Signal Processing (170 citations). Leonard E. Trigg has collaborated with scholars based in New Zealand and United Kingdom. Frequent co-authors include John G. Cleary, Geoffrey Holmes, Eibe Frank, Ian H. Witten, Sally Jo Cunningham, Mark A. Hall, Sean A. Irvine, Stuart J. Inglis, Mark Utting and Mark Hall. Their work appears in journals such as Machine Learning, Research Commons (University of Waikato), Queensland's institutional digital repository (The University of Queensland), Research Commons (The University of Waikato) and Elsevier eBooks.
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.