A Bayes point machine is a single classifier that approximates the majority decision of an ensemble of classifiers. This paper observes that kernel interpolation is a Bayes point machine for Gaussian process classification. This observation facilitates the transfer of results from both ensemble theory as well as an area of convex geometry known as Brunn-Minkowski theory to derive PAC-Bayes risk bounds for kernel interpolation. Since large margin, infinite width neural networks are kernel interpolators, the paper's findings may help to explain generalisation in neural networks more broadly. Supporting this idea, the paper finds evidence that large margin, finite width neural networks behave like Bayes point machines too.

翻译：贝耶斯点机器是一个单一的分类器,它与一组分类器的多数决定相近。 本文指出,内核内插是戈西亚过程分类的贝耶斯点机器。 这一观察有助于将共性理论和被称为布伦-明科夫斯基理论的convex几何学领域的结果转换为PAC-Bayes风险界限。 由于大边缘,无限宽神经网络是内核内插器,因此,该文件的调查结果可能有助于更广泛地解释神经网络的概括性。 支持这一想法的论文发现,大边缘、有限宽度神经网络也像拜斯点机器一样。