Georg Krempl
Georg Krempl
Information and Computing Sciences, Utrecht University, The Netherlands
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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
2412014
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
382015
The algorithm APT to classify in concurrence of latency and drift
G Krempl
International Symposium on Intelligent Data Analysis, 222-233, 2011
332011
Drift mining in data: A framework for addressing drift in classification
V Hofer, G Krempl
Computational Statistics & Data Analysis 57 (1), 377-391, 2013
272013
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
232013
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
202016
Classification in presence of drift and latency
G Krempl, V Hofer
2011 IEEE 11th International Conference on Data Mining Workshops, 596-603, 2011
202011
liobaite, I
G Krempl
Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A …, 2014
182014
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
Probabilistic active learning: Towards combining versatility, optimality and efficiency
G Krempl, D Kottke, M Spiliopoulou
International Conference on Discovery Science, 168-179, 2014
142014
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
132017
Probabilistic active learning in datastreams
D Kottke, G Krempl, M Spiliopoulou
International Symposium on Intelligent Data Analysis, 145-157, 2015
132015
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
62015
Clustering-based optimised probabilistic active learning (COPAL)
G Krempl, TC Ha, M Spiliopoulou
International Conference on Discovery Science, 101-115, 2015
52015
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
52015
Probabilistic Active Learning: A Short Proposition.
G Krempl, D Kottke, M Spiliopoulou
ECAI, 1049-1050, 2014
52014
¿ 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
5
Probabilistic active learning for active class selection
D Kottke, G Krempl, M Stecklina, CS von Rekowski, T Sabsch, TP Minh, ...
Proc. of the NIPS Workshop on the Future of Interactive Learning Machines, 2016
42016
Frontiers in Artificial Intelligence and Applications
H Fujita, E Herrera-Viedma
IOS Press: Amsterdam, The Netherlands 303, 157-170, 2018
32018
Constructing and predicting school advice for academic achievement: a comparison of item response theory and machine learning techniques
K Niemeijer, R Feskens, G Krempl, J Koops, MJS Brinkhuis
Proceedings of the Tenth International Conference on Learning Analytics …, 2020
22020
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Artículos 1–20