  Neural networks with quadratic decision functions have been introduced as
alternatives to standard neural networks with affine linear ones. They are
advantageous when the objects or classes to be identified are compact and of
basic geometries like circles, ellipses etc. In this paper we investigate the
use of such ansatz functions for classification. In particular we test and
compare the algorithm on the MNIST dataset for classification of handwritten
digits and for classification of subspecies. We also show, that the
implementation can be based on the neural network structure in the software
Tensorflow and Keras, respectively.
