Advancing Supervised Learning with the Wave Loss Function: A Robust and Smooth Approach

arXiv:2404.18101v1 Announce Type: new
Abstract: Loss function plays a vital role in supervised learning frameworks. The selection of the appropriate loss function holds the potential to have a substantial impact on the proficiency attained by the acquired model. The training of supervised learning algorithms inherently adheres to predetermined loss functions during the optimization process. In this paper, we present a novel contribution to the realm of supervised machine learning: an asymmetric loss function named wave loss. It exhibits robustness against …

abstract acquired algorithms arxiv cs.lg frameworks function impact loss robust role supervised learning training type vital