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• W1567207637 abstract "Artificial feedforward neural networks have received researchers’ great interest due to its ability to approximate functions without having a prior knowledge about the true underlying function. The most popular algorithm for training these networks is the backpropagation algorithm that is based on the minimization of the mean square error cost function. However this algorithm is not robust in the presence of outliers that may pollute the training data. In this chapter we present several methods to robustify neural network training algorithms. First, employing a family of robust statistics estimators, commonly known as M-estimators, in the backpropagation algorithm is reviewed and evaluated for the task of function approximation and dynamical model identification. As theseM-estimators sometimes do not have sufficient insensitivity to data outliers, the chapter next resorts to the statistically more robust estimator of the least median of squares, and develops a stochastic algorithm to minimize a related cost function. The reported experimental results have indeed shown the improved robustness of the new algorithm, especially compared to the standard backpropagation algorithm, on datasets with varying degrees of outlying data." @default.
• W1567207637 created "2016-06-24" @default.
• W1567207637 creator A5019125200 @default.
• W1567207637 creator A5032139239 @default.
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• W1567207637 date "2009-01-01" @default.
• W1567207637 modified "2023-10-01" @default.
• W1567207637 title "Robust Training of Artificial Feedforward Neural Networks" @default.
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• W1567207637 doi "https://doi.org/10.1007/978-3-642-01082-8_9" @default.
• W1567207637 hasPublicationYear "2009" @default.
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