  An appropriate choice of the activation function (like ReLU, sigmoid or
swish) plays an important role in the performance of (deep) multilayer
perceptrons (MLP) for classification and regression learning. Prototype-based
classification learning methods like (generalized) learning vector quantization
(GLVQ) are powerful alternatives. These models also deal with activation
functions but here they are applied to the so-called classifier function
instead. In this paper we investigate successful candidates of activation
functions known for MLPs for application in GLVQ and their influence on the
performance.
