Abstract
Recent research in genetically programmed polynomial neural networks demonstrates that they are successful in time-series modelling. Currently, an important issue is how to quantify the reliability of the inferred polynomial networks, that is how to evaluate their uncertainty in order to obtain evidence for their usefulness. This paper elaborates on approaches to estimation of error bars (confidence and prediction intervals) for polynomial neural networks including: 1) the analytical delta method, implemented using a neural network technique; 2) the empirical bootstrap method; and 3) an empirical network training method. We present results on empirical data which show that the delta method may lead to more unstable intervals and thus favour the bootstrap for practical applications.
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Nikolaev, N., Smirnov, E. (2012). Error Bars for Polynomial Neural Networks. In: Dai, H., Liu, J., Smirnov, E. (eds) Reliable Knowledge Discovery. Springer, Boston, MA. https://doi.org/10.1007/978-1-4614-1903-7_3
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DOI: https://doi.org/10.1007/978-1-4614-1903-7_3
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