Cognitive Behavior of Humans, Animals, and Machines:
Situation Model Perspectives
Oktober 2019 – Juli 2020
Leitung: Werner Schneider (Bielefeld, GER), Helge Ritter (Bielefeld, GER)
Barbara Hammer received her Ph.D. in Computer Science in 1999 and her venia legendi in Computer Science in 2003, both from the University of Osnabrueck, Germany. From 2000-2004, she was chair of the junior research group 'Learning with Neural Methods on Structured Data' at University of Osnabrueck before accepting an offer as professor for Theoretical Computer Science at Clausthal University of Technology, Germany, in 2004. Since 2010, she is holding a professorship for Machine Learning at the Faculty of Technology at Bielefeld University, Germany. Several research stays have taken her to Italy, U.K., India, France, the Netherlands, and the U.S.A. Her areas of expertise include hybrid systems, self-organizing maps, clustering, and recurrent networks as well as applications in bioinformatics, industrial process monitoring, or cognitive science. She is member of IEEE CIS and GI. She has been chairing the IEEE CIS technical committees on Data Mining, Neural Networks, and the Distinguished Lecturers Committee.
Current Main Research Interests
Barbara Hammers research interests are at the core of machine learning, in particular the development and mathematical investigation of algorithms which address non-standard settings as occur in practice, such as life-long learning, incremental learning and learning with drift, learning from structured data, unsupervised modeling and representation learning, and the like. She is interested in computational learning theory such as formal guarantees of the generalisation ability of learning models, mathematical analysis of online models, or an analysis of the representation ability of learning heuristics borrowed from nature.
Five selected publications with particular relevance to the Research Group
- Biehl, M., Abadi, F., Göpfert, C., & Hammer, B. (2019). Prototype-based classifiers in the presence of concept drift: A modelling framework. In International Workshop on Self-Organizing Maps (pp. 210-221). Springer, Cham.
- Prahm, C., Schulz, A., Paaßen, B., Schoisswohl, J., Kaniusas, E., Dorffner, G., Hammer, B., & Aszmann, O. (2019). Counteracting electrode shifts in upper-limb prosthesis control via transfer learning. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27, 956-962.
- Losing, V., Hammer, B., & Wersing, H. (2018). Incremental on-line learning: A review and comparison of state of the art algorithms. Neurocomputing, 275, 1261-1274.
- Losing, V., Hammer, B., & Wersing, H. (2017). Self-adjusting memory: how to deal with diverse drift types. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-2017), 4899-4903.
- Biehl, M., Hammer, B., & Villmann, T. (2016). Prototype-based Models for the Supervised Learning of Classification Schemes. Proceedings of the International Astronomical Union, 12, 129-138.