论文标题
关于一般随机过程中的可学习性
On Learnability under General Stochastic Processes
论文作者
论文摘要
在最坏情况下，独立和相同分布（IID）采样和在线学习理论的统计学习理论是学习理论最好的两个分支。在一般非IID随机过程下的统计学习不那么成熟。在一般的随机过程中，我们提供了功能类别的可学习性的两个自然概念。我们表明，这两个概念实际上等同于在线学习性。我们的结果适用于二元分类和回归。
Statistical learning theory under independent and identically distributed (iid) sampling and online learning theory for worst case individual sequences are two of the best developed branches of learning theory. Statistical learning under general non-iid stochastic processes is less mature. We provide two natural notions of learnability of a function class under a general stochastic process. We show that both notions are in fact equivalent to online learnability. Our results hold for both binary classification and regression.
