Optimizing extreme learning machines via ridge regression and batch intrinsic plasticity

Authors:
Klaus Neumann;Jochen J. Steil
Affiliations:
Research Institute for Cognition and Robotics (CoR-Lab), Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany;Research Institute for Cognition and Robotics (CoR-Lab), Faculty of Technology, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany
Venue:
Neurocomputing
Year:
2013

Citing 12
Cited 1

Training with noise is equivalent to Tikhonov regularization

Neural Computation
The nature of statistical learning theory

The nature of statistical learning theory
2007 Special Issue: Online reservoir adaptation by intrinsic plasticity for backpropagation-decorrelation and echo state learning

Neural Networks
Letters: Convex incremental extreme learning machine

Neurocomputing
Incremental extreme learning machine with fully complex hidden nodes

Neurocomputing
OP-ELM: Theory, Experiments and a Toolbox

ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Error minimized extreme learning machine with growth of hidden nodes and incremental learning

IEEE Transactions on Neural Networks
Batch intrinsic plasticity for extreme learning machines

ICANN'11 Proceedings of the 21th international conference on Artificial neural networks - Volume Part I
A gradient rule for the plasticity of a neuron’s intrinsic excitability

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Universal approximation using incremental constructive feedforward networks with random hidden nodes

IEEE Transactions on Neural Networks
Comments on “The Extreme Learning Machine”

IEEE Transactions on Neural Networks
Reply to “Comments on “The Extreme Learning Machine””

IEEE Transactions on Neural Networks

Intrinsic plasticity via natural gradient descent with application to drift compensation

Neurocomputing

Quantified Score

Hi-index	0.01

Visualization

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.