Interactive Machine Learning
Our expertise in Interactive Machine Learning is represented by the Visual-Interactive Machine Learning group led by Dr. Jürgen Bernard. Particular research interests in concepts, techniques, and applications is conducted along the following three pillars:
Interactive Similarity Search is the research for similarity functions for data objects or digital documents, performed in a visual-interactive and human-centered way.
Visual Analytics is one means to conduct Interactive Machine Learning. GRIS combines a broad set of competences reflected by supported data types, analysis techniques, and application areas.
Interactive Machine Learning explained
Interactive Machine Learning focuses on human-centered aspects of Machine Learning and the iterative Machine Learning process. Overall goal is to combine the strengths of humans and machines to leverage the Machine Learning process.