Leonard E. Trigg
Impact in
- Artificial Intelligence top 5%
- Machine Learning and Data Classification
- Natural Language Processing Techniques
- Information Systems top 2%
- Data Mining and Machine Learning Applications
- Software Engineering Research
- Data Mining Algorithms and Applications
Papers in
-
- Data Mining and Machine Learning Applications 5
- Data Mining Algorithms and Applications 4
-
- Machine Learning and Data Classification 2
- Bayesian Modeling and Causal Inference 2
- Co-authors
- John G. Cleary (3 shared papers)Geoffrey Holmes (4 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)Mark Utting (1 shared paper)
- Journals
- Machine Learning (1 paper)Elsevier eBooks (1 paper)Springer eBooks (1 paper)Research Commons (University of Waikato) (2 papers)
- Partner nations
- New Zealand
In The Last Decade
Leonard E. Trigg
7 papers receiving 1.3k citations
Leonard E. Trigg's Hit Papers
Peers
Comparison fields: 5 of 128
- Artificial Intelligence 424
- Information Systems 308
- Health Information Management 105
- Software 61
- Computer Science Applications 45
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 | K*: An Instance-based Learner Using an Entropic Distance Measure Hit paper breakdown → | 1995 | 609 |
| 2 | Weka: Practical machine learning tools and techniques with Java implementations Hit paper breakdown → | 1999 | 498 |
| 3 | 2000 | 201 | |
| 4 | 1998 | 63 | |
| 5 | 2007 | 55 | |
| 6 | 2001 | 3 | |
| 7 | An entropy gain measure of numeric prediction performance | 1998 | 2 |
About Leonard E. Trigg
Leonard E. Trigg is a scholar working on Information Systems, Artificial Intelligence, Statistics and Probability, Software and Nuclear Energy and Engineering, having authored 7 papers that have together received 1.4k indexed citations. Recurring topics across this work include Data Mining and Machine Learning Applications (5 papers), Data Mining Algorithms and Applications (4 papers), Statistical Methods and Applications (2 papers), Machine Learning and Data Classification (2 papers), Bayesian Modeling and Causal Inference (2 papers) and Software Testing and Debugging Techniques (1 paper). The work is most often cited by research in Artificial Intelligence (424 citations), Information Systems (308 citations), Health Information Management (105 citations), Software (61 citations) and Computer Science Applications (45 citations). Leonard E. Trigg has collaborated with scholars based in New Zealand. Frequent co-authors include John G. Cleary, Geoffrey Holmes, Ian H. Witten, Eibe Frank, Mark A. Hall, Sally Jo Cunningham, Sean A. Irvine, Mark Utting and Stuart J. Inglis. Their work appears in journals such as Machine Learning, Elsevier eBooks, Springer eBooks, Research Commons (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.