Training neural networks on noisy data

Abstract

This paper discusses approaches to noise-resistant training of MLP neural networks. We present various aspects of the issue and the ways of obtaining that goal by using two groups of approaches and combinations of them. The first group is based on a different processing of each vector depending of the likelihood of the vector being an outlier. The likelihood is determined by instance selection and outlier detection. The second group is based on training MLP neural networks with non-differentiable robust objective functions. We evaluate the performance of particular methods with different level of noise in the data for regression problems. © 2014 Springer International Publishing.