2023

Kärkkäinen, Tommi

The classical Taylor’s formula is an elementary tool in mathematical analysis and function approximation. Its role in the optimization theory, whose data-driven variants have a central role in machine learning training algorithms, is well-known. However, utilization of Taylor’s formula in the derivation of new machine learning methods is not common and the purpose of this article is to introduce such use cases. Both a feedforward neural network and a recently introduced distance-based method are used as data-driven models. We demonstrate and assess the proposed techniques empirically both in unsupervised and supervised learning scenarios.