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In this paper we propose an algorithm for Single-hidden Layer Feedforward Neural networks training. Based on the observation that the learning process of such networks can be considered to be a non-linear mapping of the training data to a high-dimensional feature space, followed by a data projection process to a low-dimensional space where classification is performed by a linear classifier, we extend the Extreme Learning Machine (ELM) algorithm in order to exploit the training data dispersiondoi:10.1109/icassp.2014.6854640 dblp:conf/icassp/IosifidisTP14 fatcat:wx34ikawmzcixmekhy7vrvkwnq