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Georg Krempl
Georg Krempl
Information and Computing Sciences, Utrecht University, The Netherlands
Verified email at uu.nl - Homepage
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Open challenges for data stream mining research
G Krempl, I Žliobaite, D Brzeziński, E Hüllermeier, M Last, V Lemaire, ...
ACM SIGKDD explorations newsletter 16 (1), 1-10, 2014
3812014
Optimised probabilistic active learning (OPAL) For fast, non-myopic, cost-sensitive active classification
G Krempl, D Kottke, V Lemaire
Machine Learning 100 (2-3), 449-476, 2015
552015
Drift mining in data: A framework for addressing drift in classification
V Hofer, G Krempl
Computational Statistics & Data Analysis 57 (1), 377-391, 2013
552013
The algorithm APT to classify in concurrence of latency and drift
G Krempl
Advances in Intelligent Data Analysis X: 10th International Symposium, IDA …, 2011
462011
Transfer learning for time series anomaly detection
V Vercruyssen, W Meert, J Davis
Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning …, 2017
422017
Classification in presence of drift and latency
G Krempl, V Hofer
2011 IEEE 11th International Conference on Data Mining Workshops, 596-603, 2011
392011
Challenges of reliable, realistic and comparable active learning evaluation
D Kottke, A Calma, D Huseljic, GM Krempl, B Sick
Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning, 2-14, 2017
382017
Multi-class probabilistic active learning
D Kottke, G Krempl, D Lang, J Teschner, M Spiliopoulou
Proceedings of the Twenty-second European Conference on Artificial …, 2016
332016
Correcting the usage of the hoeffding inequality in stream mining
P Matuszyk, G Krempl, M Spiliopoulou
International Symposium on Intelligent Data Analysis, 298-309, 2013
312013
Probabilistic active learning in datastreams
D Kottke, G Krempl, M Spiliopoulou
Advances in Intelligent Data Analysis XIV: 14th International Symposium, IDA …, 2015
272015
I. ˇZliobaite, D
G Krempl
Brzezinski, E. Hüllermeier, M. Last, V. Lemaire, T. Noack, A. Shaker, S …, 2014
252014
Toward optimal probabilistic active learning using a Bayesian approach
D Kottke, M Herde, C Sandrock, D Huseljic, G Krempl, B Sick
Machine Learning 110 (6), 1199-1231, 2021
242021
Probabilistic active learning: Towards combining versatility, optimality and efficiency
G Krempl, D Kottke, M Spiliopoulou
Discovery Science: 17th International Conference, DS 2014, Bled, Slovenia …, 2014
202014
Stream-based active learning for sliding windows under the influence of verification latency
T Pham, D Kottke, G Krempl, B Sick
Machine Learning, 1-26, 2022
172022
Online clustering of high-dimensional trajectories under concept drift
G Krempl, ZF Siddiqui, M Spiliopoulou
Machine Learning and Knowledge Discovery in Databases: European Conference …, 2011
172011
Probabilistic active learning for active class selection
D Kottke, G Krempl, M Stecklina, CS von Rekowski, T Sabsch, TP Minh, ...
arXiv preprint arXiv:2108.03891, 2021
142021
Clustering-based optimised probabilistic active learning (COPAL)
G Krempl, TC Ha, M Spiliopoulou
Discovery Science: 18th International Conference, DS 2015, Banff, AB, Canada …, 2015
122015
H¨ ullermeier
G Krempl, I Zliobaite, D Brzezinski
E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M …, 2014
102014
Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI)
ZF Siddiqui, G Krempl, M Spiliopoulou, JM Pena, N Paul, F Maestu
Brain informatics 2, 33-44, 2015
92015
How to Select Information That Matters: A Comparative Study on Active Learning Strategies for Classification
C Beyer, G Krempl, V Lemaire
15th ACM International Conference on Knowledge Technologies and Data-Driven …, 2015
92015
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