Knowledge gap detection for interactive learning of categorical knowledge
Proceedings of the 18th Computer Vision Winter Workshop, 2013
In interactive machine learning the process of labeling training instances and introducing them to the learner may be expensive in terms of human effort and time. In this paper we present different strategies for detecting gaps in the learner's knowledge and communicating these gaps to the teacher. These strategies are considered from the viewpoint of extrospective and introspective behavior of the learner -- this new perspective is also the main contribution of our paper. The experimental results indicate that the analyzed strategies are successful in reducing the number of training instances required to reach the needed recognition rate. Such a facilitation may be an important step towards the broader use of interactive autonomous systems.