Transductive Learning

Transductive learning studies prediction when the unlabeled examples to be classified are already available, so a learner can use relationships within that finite pool rather than rely only on labeled training data. Theoretical work develops generalization and mistake bounds using tools such as VC and multiclass dimensions, localized complexity, and combinatorial characterizations of online learning. Practical methods refine predictions or prototypes using the test batch, often through entropy objectives, clustering, nearest-neighbor consistency, or optimal transport, in settings including graph nodes, few-shot recognition, and audio classification. A central tension is that pool structure can improve accuracy and label discovery, but gains depend on reliable class structure and can be vulnerable to imbalance, unknown classes, or evaluation setups that expose only one fixed graph.