Transductive Linear

Transductive linear methods address learning problems where the learner has access to the entire dataset (or a significant portion) before making predictions, unlike traditional inductive methods. Current research focuses on improving the efficiency and accuracy of algorithms for transductive settings, particularly in online learning and active learning scenarios, often employing linear models and incorporating techniques like experimental design and information-based sampling. These advancements are significant for applications such as personalized online services, few-shot learning, and federated learning, where efficient and accurate learning from limited or strategically sampled data is crucial.