Goal of this research strain is the development of machine learning methods that adapt to changes on the arriving data. The KMD team works on methods for prediction, active stream learning and active feature selection over timestamped data. Some of this work strain involves experiments, namely


 Experiments I: Analysis of participant behaviour in experimental settings

Experiments on human learning

Cooperation with Leibniz Institute of Neurobiology Magdeburg

Together with the Leibniz Institute of Neurobiology Magdeburg, we analyze experiments on human learning. We also cooperate in the context of the studies profile "Learning Systems" of the bachelor degree Informatik.



Experiments II: Experiment design and analysis of human behaviour in human-machine interaction

(1) We develop learning algorithms for active and cost-aware feature/source selection on data streams. To account for the challenges of acquiring reliable labels from humans, (2) we investigate the challenges and potential of the 'pairwise comparisons' paradigm for the labeling of structured multidimensional objects. To understand the interplay of human and machine for label acquisition, (3) we design experiments where we trace human uncertainty during fully-specified tasks and underspecified tasks.

For (2) we cooperate with University Medicine Greifswald.

For (3) we additionally cooperate with TU Chemnitz and TU Ilmenau within in CHIM network, where we promote the paradigm of 'Productive Teaming' for human-machine cooperation.

Publications relating to (1,3):

Publications relating to (2):


Letzte Änderung: 21.08.2023 - Ansprechpartner: