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JMLR
2010

Semi-Supervised Learning with Max-Margin Graph Cuts

8 years 10 months ago
Semi-Supervised Learning with Max-Margin Graph Cuts
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.
Branislav Kveton, Michal Valko, Ali Rahimi, Ling H
Added 19 May 2011
Updated 19 May 2011
Type Journal
Year 2010
Where JMLR
Authors Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang
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