An ontology-based interface for machine learning

Machine learning (ML) is a complex process that can hardly be carried out by non-expert users. Especially when using adaptive systems that interpret and exploit observations of the user to modify their behavior according to the user's perceived preferences, even naïve users may be confronted with learning systems. This paper presents an approach to make non-expert users understand and influence an ML system such as to improve trust and acceptance of the overall system behavior.