Graz, Styria, Austria
The goal of the Holzinger Group HCI-KDD is to design and develop algorithms which can learn from data and improve with experience over time. However, the application of such automatic machine learning (aML) approaches in the complex health domain seems elusive in the near future, and a good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems which are trivial for human learners.
Consequently, interactive ML-approaches, by integrating a human-into-the-loop and making use of human cognitive abilities, seem to be a promising approach. iML-approaches can be of particular interest to solve problems in health informatics, where we are lacking big data sets, deal with complex data and/or rare events, where traditional learning algorithms suffer due to insufficient training samples.