It is difficult for Extreme Learning Machine (ELM) to estimate the number of hidden nodes used to match with the learning data. In this paper, a novel pruning algorithm based on sensitivity analysis is proposed for ELM. The measure to estimate the necessary number of hidden layer nodes is presented according to the defined sensitivity. When the measure is below the given threshold, the nodes with smaller sensitivities are removed from the existent network all together. Experimental results show that the proposed method can produce more compact neural network than some other existing similar algorithms. © 2013 Springer-Verlag.
CITATION STYLE
Li, Y., & Li, F. J. (2013). A pruning algorithm for extreme learning machine. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8206 LNCS, pp. 1–7). https://doi.org/10.1007/978-3-642-41278-3_1
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