Margin Classifier

Margin classifiers aim to maximize the separation between classes by creating decision boundaries with a large margin, improving classification accuracy and robustness. Current research focuses on extending margin-based approaches to handle challenges like class imbalance, heavy-tailed data distributions, and high-dimensional spaces, often employing techniques like adaptive margins, convex relaxations, and novel loss functions within various model architectures including support vector machines and deep neural networks. These advancements enhance the performance and theoretical understanding of margin classifiers, impacting diverse applications from image recognition and protein structure analysis to online learning and active domain adaptation.