Robust Parameter Estimation and Model Selection for Neural Network Regression

Part of Advances in Neural Information Processing Systems 6 (NIPS 1993)

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Yong Liu


In this paper, it is shown that the conventional back-propagation (BPP) algorithm for neural network regression is robust to lever(cid:173) ages (data with :n corrupted), but not to outliers (data with y corrupted). A robust model is to model the error as a mixture of normal distribution. The influence function for this mixture model is calculated and the condition for the model to be robust to outliers is given. EM algorithm [5] is used to estimate the parameter. The usefulness of model selection criteria is also discussed. Illustrative simulations are performed.