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Georg Krempl
Georg Krempl
Information and Computing Sciences, Utrecht University, The Netherlands
Dirección de correo verificada de uu.nl - Página principal
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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
3072014
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
522015
Drift mining in data: A framework for addressing drift in classification
V Hofer, G Krempl
Computational Statistics & Data Analysis 57 (1), 377-391, 2013
442013
The algorithm APT to classify in concurrence of latency and drift
G Krempl
International Symposium on Intelligent Data Analysis, 222-233, 2011
402011
Classification in presence of drift and latency
G Krempl, V Hofer
2011 IEEE 11th International Conference on Data Mining Workshops, 596-603, 2011
282011
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
272013
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
262017
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
262016
Probabilistic active learning in datastreams
D Kottke, G Krempl, M Spiliopoulou
International Symposium on Intelligent Data Analysis, 145-157, 2015
222015
Probabilistic active learning: Towards combining versatility, optimality and efficiency
G Krempl, D Kottke, M Spiliopoulou
International Conference on Discovery Science, 168-179, 2014
212014
Z? liobaite, I
G Krempl
BrzeziÁski, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A …, 2014
202014
Online clustering of high-dimensional trajectories under concept drift
G Krempl, ZF Siddiqui, M Spiliopoulou
Joint European Conference on Machine Learning and Knowledge Discovery in …, 2011
152011
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
102021
Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI)
ZF Siddiqui, G Krempl, M Spiliopoulou, JM Peña, N Paul, F Maestu
Brain informatics 2 (1), 33-44, 2015
102015
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
92021
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
¿ liobaite I, Brzezinski D, Hüllermeier E, Last M, Lemaire V, Noack T, Shaker A, Sievi S, Spiliopoulou M, Stefanowski J (2014) Open challenges for data stream mining research
G Krempl
SIGKDD Explor 16 (1), 1-10, 0
8
Clustering-based optimised probabilistic active learning (COPAL)
G Krempl, TC Ha, M Spiliopoulou
International Conference on Discovery Science, 101-115, 2015
72015
Brzezi nski
G Krempl, I Žliobaite
D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S …, 2014
72014
Probabilistic active learning: A short proposition
G Krempl, D Kottke, M Spiliopoulou
ECAI 2014, 1049-1050, 2014
62014
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Artículos 1–20