Given a range space (X,R), where X is a set equipped with probability measure P and the family of measurable subsets R ⊂ 2^X, and ε > 0, an epsilon-net is a subset Y of X in the support of P, which intersects each r ∈ R with P(r) ≥ ε. This talk will be
devoted to some new connections between the Learning Theory and some recent results in Computational Geometry where the epsilon-nets are studied. I will explain how some complexity measures
appearing in the theory of Active Learning rise naturally in Computational Geometry and provide certain sufficient conditions that allow to construct small epsilon-nets.
The talk is based on the joint work with A. Kupavskii
