  In this article, we improve extreme learning machines for regression tasks
using a graph signal processing based regularization. We assume that the target
signal for prediction or regression is a graph signal. With this assumption, we
use the regularization to enforce that the output of an extreme learning
machine is smooth over a given graph. Simulation results with real data confirm
that such regularization helps significantly when the available training data
is limited in size and corrupted by noise.
