TL;DR针对给定的二元假设类和分布，该研究提出了一种与最优算法相竞争的无偏主动学习算法，该算法在错误率为 η 的情况下只需要 O (m^* log |H|) 的查询次数，并且证明了超越 O (log |H|) 的开销是 NP 难的。
Abstract
For some hypothesis classes and input distributions, active agnostic learning
needs exponentially fewer samples than passive learning; for other classes and
distributions, it offers little to no improvement. The most popular algorithms
for agnostic active learning express their perform