Semi-supervised learning with max-margin graph cuts
Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang
cs.LG
Abstract
This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its generalization error. The quality of our solutions is evaluated on a synthetic problem and three UCI ML repository datasets. In most cases, we outperform manifold regularization of support vector machines, which is a state-of-the-art approach to semi-supervised max-margin learning.
Topics
Classified with taxonomy v2 on Wed, 2 Sept 2026.