Margin Based

Margin-based methods in machine learning aim to improve model generalization by maximizing the separation between classes in the feature space. Current research focuses on developing novel margin-based loss functions and active learning strategies, often within the context of deep neural networks and generalized linear models, to address challenges like class imbalance, data scarcity, and the cold-start problem. These advancements are significant because they offer improved generalization performance and efficiency in various applications, including classification, regression, and decision-making under uncertainty. The theoretical understanding of margin's role in generalization, particularly in overparameterized models, remains an active area of investigation.