Optimizing extreme learning machines via ridge regression and batch intrinsic plasticity

NEUROCOMPUTING

卷:102

Article

Neumann, Klaus¹  Steil, Jochen J.¹ 

[1] Univ Bielefeld, Fac Technol, Res Inst Cognit & Robot, CoR Lab, D-33675 Bielefeld, Germany

关键词: Neural network;    Learning;    Extreme learning machine;    Batch intrinsic plasticity;    Ridge regression;    Regularization;   

DOI  :  10.1016/j.neucom.2012.01.041

来源: Elsevier

【 摘 要 】

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. (C) 2012 Elsevier B.V. All rights reserved.

【 授权许可】

Free