Active Learning with Local Models

In this contribution, we deal with active learning, which gives the learner the power to select training samples. We propose a novel query algorithm for local learning models, a class of learners that has not been considered in the context of active learning until now. Our query algorithm is based on the idea of selecting a query on the borderline of the actual classification. This is done by drawing on the geometrical properties of local models that typically induce a Voronoi tessellation on the input space, so that the Voronoi vertices of this tessellation offer themselves as prospective query points. The performance of the new query algorithm is tested on the two-spirals problem with promising results.