Most of the semi-supervised classification methods developed so far use
unlabeled data for regularization purposes under particular distributional
assumptions such as the cluster assumption. In contrast, recently developed
methods of classification from positive and unlabeled data (PU classification)
use unlabeled data for risk evaluation, i.e., label information is directly
extracted from unlabeled data. In this paper, we extend PU classification to
also incorporate negative data and propose a novel semi-supervised
classification approach. We establish generalization error bounds for our novel
methods and show that the bounds decrease with respect to the number of
unlabeled data without the distributional assumptions that are required in
existing semi-supervised classification methods. Through experiments, we
demonstrate the usefulness of the proposed methods.

本文提出一种新的半监督分类方法，通过将无标签数据引入到分类模型中，使模型的下降边界可以与无标签数据的数量成比例而不需要假设簇的条件。经过实验证明这种方法的有效性。