We study a generalization of boosting to the multiclass setting. We introduce
a weak learning condition for multiclass classification that captures the
original notion of weak learnability as being "slightly better than random
guessing". We give a simple and efficient boosting algorithm, that does not
require realizability assumptions and its sample and oracle complexity bounds
are independent of the number of classes.
In addition, we utilize our new boosting technique in several theoretical
applications within the context of List PAC Learning. First, we establish an
equivalence to weak PAC learning. Furthermore, we present a new result on
boosting for list learners, as well as provide a novel proof for the
characterization of multiclass PAC learning and List PAC learning. Notably, our
technique gives rise to a simplified analysis, and also implies an improved
error bound for large list sizes, compared to previous results.

将增强学习推广到多类别情景中，引入了适用于多类别分类的弱学习条件，提出了一种简单高效的增强学习算法，并在列表式 PAC 学习的背景下应用其新的增强技术，证明了其可行性和可靠性。