期刊
NEUROCOMPUTING
卷 73, 期 16-18, 页码 3191-3199出版社
ELSEVIER
DOI: 10.1016/j.neucom.2010.05.022
关键词
Extreme learning machine; Constructive method; Incremental extreme learning machine; Error-minimized extreme learning machine
In this paper, we attempt to address the architectural design of ELM regressor by applying a constructive method on the basis of ELM algorithm. After the nonlinearities of ELM network are fixed by randomly generating the parameters, the network will correspond to a linear regression model. The selection of hidden nodes can then be regarded as a subset model selection in linear regression. The proposed constructive hidden nodes selection for ELM (referred to as CS-ELM) selects the optimal number of hidden nodes when the unbiased risk estimation based criterion C-p reaches the minimum value. A comparison of the proposed CS-ELM with other model selection algorithms of ELM is evaluated on several real benchmark regression applications. And the empirical study shows that CS-ELM leads to a compact network structure automatically. (C) 2010 Elsevier B.V. All rights reserved.
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