The collaborative research center SFB876 brings together data mining and
embedded systems. On the one hand, embedded systems can be further improved
using machine learning. On the other hand, data mining algorithms can
be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions
of ubiquitous systems in computing power, memory, and energy demand new
algorithms for known learning tasks. These resource bounded learning algorithms
may also be applied on extremely large data bases on servers.
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Andrea Bommert successfully defended her dissertation entitled "Integration of Feature Selection Stability in Model Fitting" on January 20, 2021. She developed measures for assessing variable selection stability as well as strategies for fitting good models using variable selection stability and applied them successfully.
The members of the doctoral committee were Prof. Dr. Jörg Rahnenführer (supervisor and first reviewer), Prof. Dr. Claus Weihs (second reviewer), Prof. Dr. Katja Ickstadt (examination chair), and Dr. Uwe Ligges ( minutes).
Andrea Bommert is a research assistant at the Faculty of Statistics and a member of the Collaborative Research Center 876 (Project A3).
We cordially congratulate her on completing her doctorate!
