论文标题
支持向量机的近乎距离的概括范围
Near-Tight Margin-Based Generalization Bounds for Support Vector Machines
论文作者
论文摘要
支持向量机（SVM）是二进制分类的最基本工具之一。在其最简单的公式中，SVM使用最大的可能边距将两类数据分离到数据中。通过众多的概括界定，对最大化边缘的关注充分激励了。在本文中，我们在边缘上重新审视并改善了经典的概括界限。此外，我们通过几乎匹配的下限来补充我们的新概括，从而几乎可以在边距方面解决SVM的概括性能。
Support Vector Machines (SVMs) are among the most fundamental tools for binary classification. In its simplest formulation, an SVM produces a hyperplane separating two classes of data using the largest possible margin to the data. The focus on maximizing the margin has been well motivated through numerous generalization bounds. In this paper, we revisit and improve the classic generalization bounds in terms of margins. Furthermore, we complement our new generalization bound by a nearly matching lower bound, thus almost settling the generalization performance of SVMs in terms of margins.
