Forward with a statistical model for modelling Neural Networks
Abstract
Even though neural networks are extensively employed in a wide range of applications, they are still regarded as black boxes and pose certain challenges in terms of dimensioning and measuring their prediction inaccuracy. This has sparked increased interest in the overlapping region between neural networks and more classic statistical approaches, which can aid in overcoming such issues. In this article, a mathematical framework linking neural networks and polynomial regression is investigated by employing a Taylor expansion technique to construct an explicit expression for the coefficients of a polynomial regression from the weights of a particular neural network. This is accomplished in regression issues using single hidden layer neural networks.
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