TL;DR本文通过推导一种新的泛化误差下限，研究了通常在经典学习理论陈述之外通常未描述的常数和瞬态区域在实践中起到的支配作用，并探讨了基于 Fourier 或小波展开替代 Taylor 展开所带来的影响，进一步研究了统计学习中的核心原则 - 平滑性如何打破维数诅咒的问题。
Abstract
A core principle in statistical learning is that smoothness of target
functions allows to break the curse of dimensionality. However, learning a
smooth function through Taylor expansions requires enough samples c