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Soft Computing
Published in: Soft Computing 9/2012

01-09-2012 | Focus

A study on random weights between input and hidden layers in extreme learning machine

Authors: Ran Wang, Sam Kwong, Xizhao Wang

Published in: Soft Computing | Issue 9/2012

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Abstract

Extreme learning machine (ELM), as an emergent technique for training feed-forward neural networks, has shown good performances on various learning domains. This paper investigates the impact of random weights during the training of ELM. It focuses on the randomness of weights between input and hidden layers, and the dimension change from input layer to hidden layer. The direct motivation is to verify as to whether during the training of ELM, the randomly assigned weights exert some positive effects. Experimentally we show that for many classification and regression problems, the dimension increase caused by random weights in ELM has a performance better than the dimension increase caused by some kernel mappings. We assume that via the random transformation, output-samples are more concentrate than input-samples which will make the learning more efficient.
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Appendix
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Metadata
Title
A study on random weights between input and hidden layers in extreme learning machine
Authors
Ran Wang
Sam Kwong
Xizhao Wang
Publication date
01-09-2012
Publisher
Springer-Verlag
Published in
Soft Computing / Issue 9/2012
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-012-0829-1

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