Georg Krempl
Impact in
- Artificial Intelligence top 5%
- Data Stream Mining Techniques
- Anomaly Detection Techniques and Applications
- Machine Learning and Data Classification
- Machine Learning and Algorithms
- Imbalanced Data Classification Techniques
- Signal Processing top 10%
- Time Series Analysis and Forecasting
- Data Management and Algorithms
Papers in
-
- Machine Learning and Data Classification 7
- Data Stream Mining Techniques 5
- Machine Learning and Algorithms 5
- Anomaly Detection Techniques and Applications 2
- Imbalanced Data Classification Techniques 1
- Intelligent Tutoring Systems and Adaptive Learning 1
-
- Time Series Analysis and Forecasting 4
- Co-authors
- Vincent Lemaire (3 shared papers)Eyke Hüllermeier (1 shared paper)Dariusz Brzeziński (1 shared paper)Jerzy Stefanowski (1 shared paper)Myra Spiliopoulou (3 shared papers)Indrė Žliobaitė (1 shared paper)Ammar Shaker (1 shared paper)Mark Last (1 shared paper)
- Journals
- Machine Learning (2 papers)Brain Informatics (1 paper)Computational Statistics & Data Analysis (1 paper)Expert Systems with Applications (1 paper)Lecture notes in computer science (1 paper)
- Partner nations
- GermanyAustriaNetherlands
In The Last Decade
Georg Krempl
13 papers receiving 295 citations
Peers
Comparison fields: 5 of 61
- Artificial Intelligence 234
- Signal Processing 65
- Computer Science Applications 17
- Computer Networks and Communications 46
- Information Systems 38
Countries citing papers authored by Georg Krempl
This map shows the geographic impact of Georg Krempl'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 Georg Krempl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georg Krempl more than expected).
Fields of papers citing papers by Georg Krempl
This network shows the impact of papers produced by Georg Krempl. 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 Georg Krempl. The network helps show where Georg Krempl may publish in the future.
Co-authors
The 17 scholars most cited alongside Georg Krempl, 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 | 2014 | 172 | |
| 2 | 2015 | 30 | |
| 3 | 2012 | 21 | |
| 4 | 2011 | 21 | |
| 5 | 2020 | 17 | |
| 6 | 2021 | 14 | |
| 7 | Challenges of Reliable, Realistic and Comparable Active Learning Evaluation | 2017 | 11 |
| 8 | 2020 | 8 | |
| 9 | 2015 | 4 | |
| 10 | 2015 | 4 | |
| 11 | Classification in Presence of Drift and Latency | 2011 | 1 |
| 12 | 2012 | 1 | |
| 13 | Temporal density extrapolation | 2015 | 1 |
| 14 | Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. | 2016 | 0 |
About Georg Krempl
Georg Krempl is a scholar working on Artificial Intelligence, Signal Processing, Control and Systems Engineering, Information Systems and Marketing, having authored 14 papers that have together received 305 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (7 papers), Data Stream Mining Techniques (5 papers), Machine Learning and Algorithms (5 papers), Time Series Analysis and Forecasting (4 papers), Anomaly Detection Techniques and Applications (2 papers), Imbalanced Data Classification Techniques (1 paper), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and Educational Assessment and Pedagogy (1 paper). The work is most often cited by research in Artificial Intelligence (234 citations), Signal Processing (65 citations), Computer Science Applications (17 citations), Computer Networks and Communications (46 citations) and Information Systems (38 citations). Georg Krempl has collaborated with scholars based in Germany, Austria and Netherlands. Frequent co-authors include Vincent Lemaire, Eyke Hüllermeier, Dariusz Brzeziński, Jerzy Stefanowski, Myra Spiliopoulou, Indrė Žliobaitė, Ammar Shaker, Mark Last, Michael R. Berthold and Bernhard Sick. Their work appears in journals such as Machine Learning, Brain Informatics, Computational Statistics & Data Analysis, Expert Systems with Applications and Lecture notes in computer science.
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.