Semi-supervised learning with max-margin graph cuts
It offers a novel max-margin approach to semi-supervised learning, improving upon a state-of-the-art method for practitioners needing better label propagation.
This paper introduces a max-margin graph cut algorithm for semi-supervised learning, proving a generalization error bound and outperforming manifold regularization of SVMs on most of four datasets.
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.