论文标题
大约足够好：尺寸和边缘复杂性的概率变体
Approximate is Good Enough: Probabilistic Variants of Dimensional and Margin Complexity
论文作者
论文摘要
我们提出并研究了维度和边缘复杂性的近似概念，这些概念与给定假设类别所需的最小维度或规范相对应。我们表明，这种概念不仅足以使用线性预测变量或内核学习，而且与确切的变体不同。因此，它们更适合讨论线性或内核方法的局限性。
We present and study approximate notions of dimensional and margin complexity, which correspond to the minimal dimension or norm of an embedding required to approximate, rather then exactly represent, a given hypothesis class. We show that such notions are not only sufficient for learning using linear predictors or a kernel, but unlike the exact variants, are also necessary. Thus they are better suited for discussing limitations of linear or kernel methods.
