Before revision:  The paper provides a theoretical argument and empirical evidence suggesting that, when the surface to approximate is smooth enough, the error of resistar classification decreases as \mathcal{O n_G^{-2}) for a grid of size n_G^d in d dimensions, whereas this error decreases as \mathcal{O .
After revision:  The paper provides a theoretical argument and empirical evidence suggesting that, when the surface to approximate is smooth enough, the error of resistar classification decreases as \mathcal{O n_G^{-2}) , where n_G is the number of points on each axis of the grid. The approximation in cubes requires computing less boundary points than the approximation in simplices but the latter is always a manifold and is more accurate for a given value of n_G. The paper reports tests of the method when varying n_G and the dimensionality of the space (up to 9) .