Thomas Petsche
Impact in
- Artificial Intelligence top 10%
- Neural Networks and Applications
- Data Stream Mining Techniques
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
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- Face and Expression Recognition
Papers in
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- Machine Learning and Algorithms 3
- Neural Networks and Applications 3
- Algorithms and Data Compression 3
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- Advanced Control Systems Optimization 1
- Co-authors
- Bernhard Schölkopf (1 shared paper)Anthony Kuh (3 shared papers)Ronald L. Rivest (2 shared papers)B. Dickinson (1 shared paper)Stephen José Hanson (4 shared papers)Christian J. Darken (1 shared paper)N.I. Santoso (1 shared paper)Gary M. Kuhn (1 shared paper)
- Journals
- Conference on Learning Theory (1 paper)IEEE Transactions on Neural Networks (1 paper)Neural Information Processing Systems (2 papers)Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) (1 paper)neural information processing systems (2 papers)
- Partner nations
- United StatesGermany
In The Last Decade
Thomas Petsche
10 papers receiving 258 citations
Peers
Comparison fields: 5 of 66
- Artificial Intelligence 194
- Computer Vision and Pattern Recognition 103
- Signal Processing 30
- Media Technology 18
- Control and Systems Engineering 46
Countries citing papers authored by Thomas Petsche
This map shows the geographic impact of Thomas Petsche'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 Thomas Petsche with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Petsche more than expected).
Fields of papers citing papers by Thomas Petsche
This network shows the impact of papers produced by Thomas Petsche. 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 Thomas Petsche. The network helps show where Thomas Petsche may publish in the future.
Co-authors
The 10 scholars most cited alongside Thomas Petsche, 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 | Improving the accuracy and speed of support vector learning machines | 1997 | 156 |
| 2 | Learning Time-varying Concepts | 1990 | 47 |
| 3 | A Neural Network Autoassociator for Induction Motor Failure Prediction | 1995 | 32 |
| 4 | 1990 | 22 | |
| 5 | Incrementally Learning Time-varying Half-planes | 1991 | 7 |
| 6 | A Trellis-Structured Neural Network | 1987 | 5 |
| 7 | Computational learning theory and natural learning systems: Volume IV: making learning systems practical | 1997 | 4 |
| 8 | 2005 | 3 | |
| 9 | Initializing Neural Networks Using Decision Trees | 1997 | 2 |
| 10 | N-Learners Problem: System of PAC Learners | 1997 | 1 |
About Thomas Petsche
Thomas Petsche is a scholar working on Artificial Intelligence, Control and Systems Engineering, Electrical and Electronic Engineering, Computer Networks and Communications and Molecular Biology, having authored 10 papers that have together received 279 indexed citations. Recurring topics across this work include Machine Learning and Algorithms (3 papers), Neural Networks and Applications (3 papers), Algorithms and Data Compression (3 papers), Advanced Memory and Neural Computing (2 papers), Machine Learning in Bioinformatics (1 paper), Optimization and Search Problems (1 paper), Advanced Control Systems Optimization (1 paper) and CCD and CMOS Imaging Sensors (1 paper). The work is most often cited by research in Artificial Intelligence (194 citations), Computer Vision and Pattern Recognition (103 citations), Signal Processing (30 citations), Media Technology (18 citations) and Control and Systems Engineering (46 citations). Thomas Petsche has collaborated with scholars based in United States and Germany. Frequent co-authors include Bernhard Schölkopf, Anthony Kuh, Ronald L. Rivest, B. Dickinson, Stephen José Hanson, Christian J. Darken, N.I. Santoso, Gary M. Kuhn, Bradley W. Dickinson and Russell Greiner. Their work appears in journals such as Conference on Learning Theory, IEEE Transactions on Neural Networks, Neural Information Processing Systems, Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) and neural information processing systems.
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.