Helge Langseth
- Artificial Intelligence top 2%
- Statistics, Probability and Uncertainty top 0.5%
- Information Systems top 2%
- Safety, Risk, Reliability and Quality top 1%
- Software top 2%
- Co-authors
- Luigi PortinaleThomas D. NielsenHeri RamampiaroAntonio SalmerónGeorgios PitsilisRafael RumíAnders L. MadsenBo Henry Lindqvist
- Topics
- Bayesian Modeling and Causal Inference (30 papers)Bayesian Methods and Mixture Models (8 papers)AI-based Problem Solving and Planning (8 papers)
In The Last Decade
Helge Langseth
66 papers receiving 1.5k citations
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 797
- Statistics, Probability and Uncertainty 431
- Information Systems 266
- Safety, Risk, Reliability and Quality 254
- Software 246
Countries citing papers authored by Helge Langseth
This map shows the geographic impact of Helge Langseth'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 Helge Langseth with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Helge Langseth more than expected).
Fields of papers citing papers by Helge Langseth
This network shows the impact of papers produced by Helge Langseth. 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 Helge Langseth. The network helps show where Helge Langseth may publish in the future.
Co-authorship network of co-authors of Helge Langseth
This figure shows the co-authorship network connecting the top 25 collaborators of Helge Langseth. A scholar is included among the top collaborators of Helge Langseth based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Helge Langseth. Helge Langseth is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 5 | |
| 3 | 1 | |
| 4 | 11 | |
| 5 | 8 | |
| 6 | 11 | |
| 7 | 7 | |
| 8 | Data driven case base construction for prediction of success of marine operations | 1 |
| 9 | 6 | |
| 10 | 60 | |
| 11 | 20 | |
| 12 | 48 | |
| 13 | 28 | |
| 14 | Towards a more expressive model for dynamic classification | 3 |
| 15 | Parameter Estimation in Mixtures of Truncated Exponentials | 2 |
| 16 | 8 | |
| 17 | Fusion of domain knowledge with data for structural learning in object oriented domains | 31 |
| 18 | Structural Learning in Object Oriented Domains | 11 |
| 19 | Heuristics for Two Extensions of Basic Troubleshooting | 17 |
| 20 | Learning Retrieval Knowledge from Data | 5 |
About Helge Langseth
Helge Langseth is a scholar working on Artificial Intelligence, Software and Health Informatics, having authored 67 papers that have together received 1.6k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (30 papers), Bayesian Methods and Mixture Models (8 papers) and AI-based Problem Solving and Planning (8 papers). The work is most often cited by research in Software (246 citations), Statistics, Probability and Uncertainty (431 citations) and Safety, Risk, Reliability and Quality (254 citations). Helge Langseth has collaborated with scholars based in Norway, Denmark and Spain. Frequent co-authors include Luigi Portinale, Thomas D. Nielsen, Heri Ramampiaro, Antonio Salmerón, Georgios Pitsilis, Rafael Rumí, Anders L. Madsen, Bo Henry Lindqvist, Basant Agarwal and Agnar Aamodt. Their work appears in journals such as IEEE Access, IEEE Transactions on Smart Grid and Pattern Recognition.
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.