svm-transductive-semi-supervised-learning

IN premise — entries/2026/06/21/wiki-Support_vector_machine-chunk-5.md

Created 2026-06-21T09:55:54+00:00

Transductive SVMs (Vapnik, 1998) extend SVMs to semi-supervised learning by jointly optimizing the separating hyperplane and the labels of unlabeled test data, where test labels y* are decision variables in the optimization.

Dependents

These beliefs depend on this one:

• IN svm-framework-extends-beyond-binary-classification — The SVM framework extends well beyond its original binary classification setting — SVR adapts the max-margin principle to regression via epsilon-insensitive loss, transductive SVMs bridge to semi-supervised learning by jointly optimizing over labeled and unlabeled data, and Bayesian SVMs reinterpret the framework probabilistically for automatic hyperparameter tuning with uncertainty quantification.