Robust regression and outlier detection
Robust regression and outlier detection
Multilayer feedforward networks are universal approximators
Neural Networks
Neural network design
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
TAO-robust backpropagation learning algorithm
Neural Networks
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Robust MCD-Based Backpropagation Learning Algorithm
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Robust error measure for supervised neural network learning with outliers
IEEE Transactions on Neural Networks
The annealing robust backpropagation (ARBP) learning algorithm
IEEE Transactions on Neural Networks
A robust backpropagation learning algorithm for function approximation
IEEE Transactions on Neural Networks
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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.