Comparing different learning approaches in categorical knowledge acquisition
Proceedings of the 2012 Computer Vision Winter Workshop (CVWW), 2012
In this paper we address the problem of acquiring categorical knowledge from the active learning perspective. We describe and implement several teacher and learner-driven approaches that require different levels of teacher competencies and consider different types of knowledge for selection of training samples. The experimental results show that the active learning approach outperforms the passive one and that the adaptation of the learning process to the learner’s knowledge significantly improves the learning performance.