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.

翻译：本文提出了一种新颖的半监督学习算法。该算法学习图割，以最大化与调和函数解所诱导标签之间的间隔。我们对这一方法进行了理论分析，将其与现有工作进行了比较，并证明了其泛化误差的上界。通过在一个人工合成问题以及三个UCI机器学习库数据集上评估解决方案的质量，在大多数情况下，我们的方法优于支持向量机的流形正则化——后者是目前最先进的半监督最大间隔学习方法。