Peter Haider
Impact in
- Artificial Intelligence top 10%
- Text and Document Classification Technologies
- Natural Language Processing Techniques
- Topic Modeling
- Internet Traffic Analysis and Secure E-voting
- Information Systems top 10%
- Web Data Mining and Analysis
- Spam and Phishing Detection
- Information Retrieval and Search Behavior
Papers in
-
- Bayesian Methods and Mixture Models 3
- Algorithms and Data Compression 2
- Machine Learning and Algorithms 2
- Text and Document Classification Technologies 2
- Internet Traffic Analysis and Secure E-voting 1
-
- Spam and Phishing Detection 4
- Web Data Mining and Analysis 1
- Co-authors
- Tobias Scheffer (7 shared papers)Steffen Bickel (2 shared papers)Ulf Brefeld (2 shared papers)David S. Vogel (1 shared paper)Samit Bhattacharya (1 shared paper)A. Basu (1 shared paper)Michael Brückner (1 shared paper)
- Journals
- publish.UP (University of Potsdam) (1 paper)ACM SIGKDD Explorations Newsletter (1 paper)Max Planck Institute for Plasma Physics (2 papers)arXiv (Cornell University) (1 paper)
In The Last Decade
Peter Haider
9 papers receiving 131 citations
Peers
Comparison fields: 5 of 33
- Artificial Intelligence 113
- Information Systems 59
- Human-Computer Interaction 9
- Signal Processing 17
- Computer Vision and Pattern Recognition 23
Countries citing papers authored by Peter Haider
This map shows the geographic impact of Peter Haider'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 Peter Haider with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Haider more than expected).
Fields of papers citing papers by Peter Haider
This network shows the impact of papers produced by Peter Haider. 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 Peter Haider. The network helps show where Peter Haider may publish in the future.
Co-authors
The 7 scholars most cited alongside Peter Haider, 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 | 2005 | 30 | |
| 2 | 2005 | 28 | |
| 3 | 2007 | 27 | |
| 4 | 2008 | 23 | |
| 5 | 2005 | 11 | |
| 6 | 2012 | 11 | |
| 7 | 2009 | 9 | |
| 8 | 2012 | 2 | |
| 9 | Highly Scalable Discriminative Spam Filtering. | 2006 | 1 |
About Peter Haider
Peter Haider is a scholar working on Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics, Computer Networks and Communications and Occupational Therapy, having authored 9 papers that have together received 142 indexed citations. Recurring topics across this work include Spam and Phishing Detection (4 papers), Bayesian Methods and Mixture Models (3 papers), Algorithms and Data Compression (2 papers), Machine Learning and Algorithms (2 papers), Complex Network Analysis Techniques (2 papers), Text and Document Classification Technologies (2 papers), Web Data Mining and Analysis (1 paper) and Internet Traffic Analysis and Secure E-voting (1 paper). The work is most often cited by research in Artificial Intelligence (113 citations), Information Systems (59 citations), Human-Computer Interaction (9 citations), Signal Processing (17 citations) and Computer Vision and Pattern Recognition (23 citations). Peter Haider has collaborated with scholars based in Germany, India and Spain. Frequent co-authors include Tobias Scheffer, Steffen Bickel, Ulf Brefeld, David S. Vogel, Samit Bhattacharya, A. Basu and Michael Brückner. Their work appears in journals such as publish.UP (University of Potsdam), ACM SIGKDD Explorations Newsletter, Max Planck Institute for Plasma Physics and arXiv (Cornell University).
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.