We consider a document classification problem where document labels are
absent but only relevant keywords of a target class and unlabeled documents are
given. Although heuristic methods based on pseudo-labeling have been
considered, theoretical understanding of this problem has still been limited.
Moreover, previous methods cannot easily incorporate well-developed techniques
in supervised text classification. In this paper, we propose a theoretically
guaranteed learning framework that is simple to implement and has flexible
choices of models, e.g., linear models or neural networks. We demonstrate how
to optimize the area under the receiver operating characteristic curve (AUC)
effectively and also discuss how to adjust it to optimize other well-known
evaluation metrics such as the accuracy and F1-measure. Finally, we show the
effectiveness of our framework using benchmark datasets.

本文提出了一个理论上有保证的学习框架，用于无标签文档的分类问题，该框架可以灵活选择模型，并且有效地优化了接收器操作特征曲线下的面积，并在基准数据集上展示了其有效性。

仅从相关关键词和未标记文档中学习

Learning Only from Relevant Keywords and Unlabeled Documents

This tutorial provides a gentle introduction to kernel density estimation
(KDE) and recent advances regarding confidence bands and geometric/topological
features. We begin with a discussion of basic properties of KDE: the
convergence rate under various metrics, density derivative estimation, and
bandwidth selection. Then, we introduce common approaches to the construction
of confidence intervals/bands, and we discuss how to handle bias. Next, we talk
about recent advances in the inference of geometric and topological features of
a density function using KDE. Finally, we illustrate how one can use KDE to
estimate a cumulative distribution function and a receiver operating
characteristic curve. We provide R implementations related to this tutorial at
the end.

本篇论文是一篇介绍核密度估计及其最近在置信区间，几何 / 拓扑特征方面的新进展的入门教程。论文从基本的 KDE 属性开始，介绍了收敛速率、密度导数估计和带宽选择等内容，接着介绍了常见的置信区间 / 带的构造方法以及如何处理偏差。然后，我们讨论了最近使用 KDE 推断密度函数的几何和拓扑特征的进展和如何用 KDE 估计累积分布函数和接收操作特征曲线。 文中提供相关的 R 实现。