TL;DR本文提出了一种半监督集成学习方法,利用未标记的数据增加基学习器之间的差异性和数据多样性,从而在 labeled data 上提高基学习器的准确性,在 unlabeled data 上最大化学习器之间的差异性。实验证明这种方法有效利用 unlabeled data 进行集成学习并且具有很高的竞争力。
Abstract
ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlab