Transductive Support Vector Machines (TSVMs) are a variant of Support Vector Machines designed to improve generalization by leveraging both labeled and unlabeled data during training, focusing on minimizing errors on a specific test set. Unlike inductive learning, TSVMs aim to directly optimize the decision boundary for a particular set of test instances, making them particularly effective in semi-supervised learning scenarios.