On improving the conditioning of extreme learning machine: A linear case
- 1 December 2009
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 1-5
Abstract
Recently Extreme Learning Machine (ELM) has been attracting attentions for its simple and fast training algorithm, which randomly selects input weights. Given sufficient hidden neurons, ELM has a comparable performance for a wide range of regression and classification problems. However, in this paper we argue that random input weight selection may lead to an ill-conditioned problem, for which solutions will be numerically unstable. In order to improve the conditioning of ELM, we propose an input weight selection algorithm for an ELM with linear hidden neurons. Experiment results show that by applying the proposed algorithm accuracy is maintained while condition is perfectly stable.Keywords
This publication has 2 references indexed in Scilit:
- Enhanced Extreme Learning Machine with stacked generalization2008
- Stacked regressionsMachine Learning, 1996