A Regret Bound for the AdaMax Algorithm With Image Segmentation Application

ABSTRACT The AdaMax algorithm provides enhanced convergence properties for stochastic optimization problems. In this paper, we present a regret bound for the AdaMax algorithm, offering a tighter and more refined analysis compared to existing bounds. This theoretical advancement provides deeper insights into the optimization landscape of machine learning algorithms. Specifically, the You Only Look Once (YOLO) framework has become well‐known as an extremely effective object segmentation tool, mostly because of its extraordinary accuracy in real‐time processing, which makes it a preferred option for many computer vision applications. Finally, we used this algorithm for image segmentation.