论文标题
最佳近似变得容易
Optimal Approximations Made Easy
论文作者
论文摘要
Li，Long和Srinivasan对设定系统近似的基本结果已成为多个社区的关键工具，例如学习理论，算法，计算几何，组合学和数据分析。 本文的目的是为有限设置系统提供模块化，独立的，直观的证明。我们假设的唯一成分是标准Chernoff的浓度结合。这使得更广泛的受众可以访问证明，读者不熟悉统计学习理论的技术，并可以在几何学，算法或组合学课程中进行单个独立的演讲中涵盖。
The fundamental result of Li, Long, and Srinivasan on approximations of set systems has become a key tool across several communities such as learning theory, algorithms, computational geometry, combinatorics and data analysis. The goal of this paper is to give a modular, self-contained, intuitive proof of this result for finite set systems. The only ingredient we assume is the standard Chernoff's concentration bound. This makes the proof accessible to a wider audience, readers not familiar with techniques from statistical learning theory, and makes it possible to be covered in a single self-contained lecture in a geometry, algorithms or combinatorics course.
