论文标题
基于图的半监督学习中未标记的数据有助于：贝叶斯非参数透视图
Unlabeled Data Help in Graph-Based Semi-Supervised Learning: A Bayesian Nonparametrics Perspective
论文作者
论文摘要
在本文中，我们分析了基于图形的基于图的方法，在多种假设下进行了半监督学习。我们采用了贝叶斯的观点，并证明，对于以足够多的未标记数据构建的合适的先验选择，以最小值最佳的速率达到了真相的后签合同，直至对数因素。我们的理论涵盖了回归和分类。
In this paper we analyze the graph-based approach to semi-supervised learning under a manifold assumption. We adopt a Bayesian perspective and demonstrate that, for a suitable choice of prior constructed with sufficiently many unlabeled data, the posterior contracts around the truth at a rate that is minimax optimal up to a logarithmic factor. Our theory covers both regression and classification.
