Abstract
Training data sets containing outliers are often a problem for supervised neural networks learning algorithms. They may not always come up with acceptable performance and build very inaccurate models. In this paper new, robust to outliers, learning algorithm based on the Least Trimmed Squares (LTS) estimator is proposed. The LTS learning algorithm is simultaneously the first robust learning algorithm that takes into account not only gross errors but also leverage data points. Results of simulations of networks trained with the new algorithm are presented and the robustness against outliers is demonstrated.
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Rusiecki, A. (2007). Robust LTS Backpropagation Learning Algorithm. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_13
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DOI: https://doi.org/10.1007/978-3-540-73007-1_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73006-4
Online ISBN: 978-3-540-73007-1
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