Empirical Investigation of Learning Curves
Assessing Convexity Characteristics
K. Gogora (TU Delft - Electrical Engineering, Mathematics and Computer Science)
T.J. Viering – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
J.H. Krijthe – Mentor (TU Delft - Pattern Recognition and Bioinformatics)
Z. Yue – Graduation committee member (TU Delft - Multimedia Computing)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Nonconvexity in learning curves is almost always undesirable. A machine learning model with a non-convex learning curve either requires a larger quantity of data to observe progress in its accuracy or experiences an exponential decrease of accuracy at low sample sizes, with no improvement in accuracy even when more data points are added. This paper proposes a novel approach to determine the convexity of a learning curve, which relies on calculating the second derivative of the learning curve to estimate its convexity. Along the way, we have confirmed the correctness of the proposed method from multiple perspectives, such as testing it with baselines or establishing confidence intervals for the convexity of the learning curve. Lastly, we compare our method to an alternative method and highlight some of its shortcomings.