TSVM is an extension of standard support vector machines with unlabeled data. In a standard SVM only the labeled
data is used, and the goal is to find a maximum margin linear boundary in the Reproducing Kernel Hibert Space. In a
TSVM the unlabeled data is also used, and the goal is to find a labeling of the unlabeled data, so that a linear
boundary has the maximum margin on both the origianl labeled data and the (now labeled) unlabeled data. The
decision boundary has the smallest generalization error bound on unlabeled data.
Reference:
Xiaojin Zhu. Semi-Supervised Learning with Graphs
