Christoph Raab
- Artificial Intelligence top 10%
- Atomic and Molecular Physics, and Optics
- Computer Networks and Communications
- Electrical and Electronic Engineering
- Management Science and Operations Research
- Co-authors
- Frank-Michael SchleifF. Schmidt‐KalerPavel BushevChristoph BecherR. BlattJ. EschnerPeter TiňoStephan Falke
- Topics
- Data Stream Mining Techniques (5 papers)Face and Expression Recognition (5 papers)Anomaly Detection Techniques and Applications (3 papers)
- Cited by
- Artificial IntelligenceAtomic and Molecular Physics, and OpticsComputer Networks and Communications
- Partner nations
- GermanyNetherlandsUnited Kingdom
In The Last Decade
Christoph Raab
21 papers receiving 202 citations
Peers
Comparison fields: 5 of 50
- Artificial Intelligence 141
- Atomic and Molecular Physics, and Optics 53
- Computer Networks and Communications 36
- Electrical and Electronic Engineering 32
- Management Science and Operations Research 16
Countries citing papers authored by Christoph Raab
This map shows the geographic impact of Christoph Raab'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 Christoph Raab with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Christoph Raab more than expected).
Fields of papers citing papers by Christoph Raab
This network shows the impact of papers produced by Christoph Raab. 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 Christoph Raab. The network helps show where Christoph Raab may publish in the future.
Co-authorship network of co-authors of Christoph Raab
This figure shows the co-authorship network connecting the top 25 collaborators of Christoph Raab. A scholar is included among the top collaborators of Christoph Raab 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 Christoph Raab. Christoph Raab 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 | 3 | |
| 3 | 1 | |
| 4 | 9 | |
| 5 | 6 | |
| 6 | 7 | |
| 7 | 2 | |
| 8 | 6 | |
| 9 | 2 | |
| 10 | 1 | |
| 11 | 82 | |
| 12 | 7 | |
| 13 | 8 | |
| 14 | Reactive Soft Prototype Computing for frequent reoccurring Concept Drift. | 6 |
| 15 | 5 | |
| 16 | 6 | |
| 17 | Transfer learning for the probabilistic classification vector machine | 2 |
| 18 | 1 | |
| 19 | 1 | |
| 20 | 48 |
About Christoph Raab
Christoph Raab is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Graphics and Computer-Aided Design, having authored 23 papers that have together received 210 indexed citations. Recurring topics across this work include Data Stream Mining Techniques (5 papers), Face and Expression Recognition (5 papers) and Anomaly Detection Techniques and Applications (3 papers). The work is most often cited by research in Artificial Intelligence (141 citations), Atomic and Molecular Physics, and Optics (53 citations) and Computer Networks and Communications (36 citations). Christoph Raab has collaborated with scholars based in Germany, Netherlands and United Kingdom. Frequent co-authors include Frank-Michael Schleif, F. Schmidt‐Kaler, Pavel Bushev, Christoph Becher, R. Blatt, J. Eschner, Peter Tiňo, Stephan Falke, Christian Nölleke and Michael Biehl. Their work appears in journals such as Physical Review Letters, Neurocomputing and Pattern Recognition Letters.
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.