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Neural Processing Letters

, Volume 36, Issue 2, pp 145–160

First Online: 15 May 2012

Abstract

Outliers and gross errors in training data sets can seriously deteriorate the performance of traditional supervised feedforward neural networks learning algorithms. This is why several learning methods, to some extent robust to outliers, have been proposed. In this paper we present a new robust learning algorithm based on the iterative Least Median of Squares, that outperforms some existing solutions in its accuracy or speed. We demonstrate how to minimise new non-differentiable performance function by a deterministic approximate method. Results of simulations and comparison with other learning methods are demonstrated. Improved robustness of our novel algorithm, for data sets with varying degrees of outliers, is shown.

KeywordsFeedforward neural networks Robust learning algorithms Outliers Robust statistics  Download to read the full article text



Author: Andrzej Rusiecki

Source: https://link.springer.com/







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