提出一种新半监督学习算法，通过最大化标签边距提升分类效果。
Semi-supervised learning with max-margin graph cuts
- 基于调和函数解构造图切割，以最大化标签边距
- 在三个真实数据集上优于主流的流形正则化SVM方法
- 理论证明泛化误差有界，适合小样本场景
本文提出一种新型半监督学习算法，该算法学习的图切割能最大化由调和函数解诱导的标签边距。我们论证了该方法的合理性，与现有工作进行了对比，并证明了其泛化误差的上界。通过合成问题及三个UCI机器学习数据集验证了解法质量。在多数情况下，本方法优于当前最先进的半监督最大边距学习方法——支持向量机的流形正则化。
原文摘要 · Abstract (English)
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.
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