Different approaches to fitting and extrapolating the learning curve

Abstract

Extrapolation of the learning curve provides an estimation of how much data is needed to achieve the desired performance. It can be beneficial when gathering data is complex, or computation resource is limited. One of the essential processes of learning curve extrapolation is curve fitting. This research first analyses the behaviour of existing curve fitting methods such as Newton, Levenberg-Marquardt and Evolutionary algorithms when fitting different function models on learning curves. Furthermore, it also illustrates a few techniques to improve the learning curve fitting and extrapolation procedure.