Article ID Journal Published Year Pages File Type
407557 Neurocomputing 2013 8 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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