Niels Landwehr
- Artificial Intelligence top 2%
- Machine Learning and Algorithms 9
- Bayesian Modeling and Causal Inference 8
- Natural Language Processing Techniques 7
- Machine Learning and Data Classification 6
- Algorithms and Data Compression 5
- Signal Processing top 5%
- Data Management and Algorithms 3
- Geophysics top 10%
- Information Systems top 5%
- Data Mining Algorithms and Applications 10
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- Smart Agriculture and AI 4
- Co-authors
- Eibe FrankMark HallLuc De RaedtTobias SchefferNicolas KuehnNorman AbrahamsonPaolo FrasconiKristian Kersting
- Journals
- Machine Learning (5 papers)Computers and Electronics in Agriculture (3 papers)Bulletin of the Seismological Society of America (2 papers)
- Partner nations
- GermanyBelgiumUnited States
In The Last Decade
Niels Landwehr
43 papers receiving 1.8k citations
Hit Papers
Peers
Comparison fields: 5 of 171
- Artificial Intelligence 651
- Signal Processing 139
- Geophysics 139
- Information Systems 235
- Civil and Structural Engineering 201
Countries citing papers authored by Niels Landwehr
This map shows the geographic impact of Niels Landwehr'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 Niels Landwehr with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Niels Landwehr more than expected).
Fields of papers citing papers by Niels Landwehr
This network shows the impact of papers produced by Niels Landwehr. 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 Niels Landwehr. The network helps show where Niels Landwehr may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Niels Landwehr, 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 | 2022 | 10 | |
| 2 | 2021 | 45 | |
| 3 | 2020 | 19 | |
| 4 | 2020 | 15 | |
| 5 | 2020 | 38 | |
| 6 | 2019 | 141 | |
| 7 | A Non-ergodic GMPE for Europe and the Middle East with Spatially Varying Coefficients | 2019 | 2 |
| 8 | Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2016) | 2016 | 15 |
| 9 | Learning to identify concise regular expressions that describe email campaigns | 2015 | 9 |
| 10 | Learning to Identify Regular Expressions that Describe Email Campaigns | 2012 | 5 |
| 11 | Active Comparison of Prediction Models | 2012 | 2 |
| 12 | Active Estimation of F-Measures | 2010 | 11 |
| 13 | 2009 | 4 | |
| 14 | Probabilistic logical sequence learning for video | 2009 | 5 |
| 15 | 2008 | 18 | |
| 16 | r-grams: relational grams | 2007 | 3 |
| 17 | 2007 | 36 | |
| 18 | 2007 | 15 | |
| 19 | kFOIL: learning simple relational kernels | 2006 | 46 |
| 20 | Constrained hidden Markov models for population-based haplotyping (Extended Abstract) | 2006 | 1 |
About Niels Landwehr
Niels Landwehr is a scholar working on Artificial Intelligence, Process Chemistry and Technology and Signal Processing, having authored 45 papers that have together received 1.9k indexed citations. Recurring topics across this work include Data Mining Algorithms and Applications (10 papers), Machine Learning and Algorithms (9 papers), Bayesian Modeling and Causal Inference (8 papers), Natural Language Processing Techniques (7 papers), Machine Learning and Data Classification (6 papers), Algorithms and Data Compression (5 papers), Smart Agriculture and AI (4 papers) and Data Management and Algorithms (3 papers). The work is most often cited by research in Artificial Intelligence (651 citations), Signal Processing (139 citations) and Geophysics (139 citations). Niels Landwehr has collaborated with scholars based in Germany, Belgium and United States. Frequent co-authors include Eibe Frank, Mark Hall, Luc De Raedt, Tobias Scheffer, Nicolas Kuehn, Norman Abrahamson, Paolo Frasconi, Kristian Kersting, Hanna Drimalla and Isabel Dziobek. Their work appears in journals such as Machine Learning, Computers and Electronics in Agriculture, Bulletin of the Seismological Society of America, BMC Bioinformatics and Journal of Machine Learning Research.
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.