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Erschienen in:
01.01.2016 | Extreme Learning Machine and Applications
Applying a new localized generalization error model to design neural networks trained with extreme learning machine
Erschienen in: Neural Computing and Applications | Ausgabe 1/2016
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Abstract
High accuracy and low overhead are two key features of a well-designed classifier for different classification scenarios. In this paper, we propose an improved classifier using a single-hidden layer feedforward neural network (SLFN) trained with extreme learning machine. The novel classifier first utilizes principal component analysis to reduce the feature dimension and then selects the optimal architecture of the SLFN based on a new localized generalization error model in the principal component space. Experimental and statistical results on the NSL-KDD data set demonstrate that the proposed classifier can achieve a significant performance improvement compared with previous classifiers.