TY - JOUR A1 - Seitz, Philipp A1 - Schmitt, Jan T1 - Alternating Transfer Functions to Prevent Overfitting in Non-Linear Regression with Neural Networks T2 - Journal of Experimental & Theoretical Artificial Intelligence N2 - In nonlinear regression with machine learning methods, neural networks (NNs) are ideally suited due to their universal approximation property, which states that arbitrary nonlinear functions can thereby be approximated arbitrarily well. Unfortunately, this property also poses the problem that data points with measurement errors can be approximated too well and unknown parameter subspaces in the estimation can deviate far from the actual value (so-called overfitting). Various developed methods aim to reduce overfitting through modifications in several areas of the training. In this work, we pursue the question of how an NN behaves in training with respect to overfitting when linear and nonlinear transfer functions (TF) are alternated in different hidden layers (HL). The presented approach is applied to a generated dataset and contrasted to established methods from the literature, both individually and in combination. Comparable results are obtained, whereby the common use of purely nonlinear transfer functions proves to be not recommended generally. KW - Machine learning; nonlinear regression; function approximation; overfitting; transfer function Y1 - 2023 UR - https://opus4.kobv.de/opus4-fhws/frontdoor/index/index/docId/4919 UR - https://nbn-resolving.org/urn:nbn:de:bvb:863-opus-49199 UR - https://doi.org/10.1080/0952813X.2023.2270995 ER -