Among many approaches to choosing the proper size of neural networks, one popular approach is to start with an oversized network and then prune it to a smaller size so as to attain better performance with less computational complexity. In this paper, a new hidden node pruning method is proposed based on the redundancy reduction among hidden nodes. The redundancy information is given by correlation coefficients among hidden nodes and this can save computational complexity. Experimental results demonstrate the effectiveness of the proposed method. © 2011 Springer-Verlag.
CITATION STYLE
Oh, S. H. (2011). Hidden node pruning of multilayer perceptrons based on redundancy reduction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6935 LNCS, pp. 245–249). https://doi.org/10.1007/978-3-642-24082-9_30
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