Classification with neural networks with quadratic decision functions
Leon Frischauf Otmar Scherzer Cong Shi
Neural network with quadratic decision functions have been introduced as
alternatives to standard neural networks with affine linear one. They are
advantageous when the objects to be identified are of compact basic geometries
like circles, ellipsis 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.