کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
412722 | 679678 | 2010 | 9 صفحه PDF | دانلود رایگان |

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 CP 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.
Journal: Neurocomputing - Volume 73, Issues 16–18, October 2010, Pages 3191–3199