The Vapnik-Chervonenkis dimension of cubes in $\mathbb{R}^d$
  The Vapnik-Chervonenkis (VC) dimension of a collection of subsets of a set is an important combinatorial concept in settings such as discrete geometry and machine learning. In this paper we prove that the VC dimension of the family of $d$-dimensional cubes in $\mathbb R^d$ is $\lfloor(3d+1)/2\rfloor$. 
Christian J. J. Despres
math.CO math.MG stat.ML