Optimizing extreme learning machines via ridge regression and batch intrinsic plasticity

Extreme learning machines are randomly initialized single-hidden layer feed-forward neural networks where the training is restricted to the output weights in order to achieve fast learning with good performance. This contribution shows how batch intrinsic plasticity, a novel and efficient scheme for input specific tuning of non-linear transfer functions, and ridge regression can be combined to optimize extreme learning machines without searching for a suitable hidden layer size. We show that our scheme achieves excellent performance on a number of standard regression tasks and regression applications from robotics.