  Transfer learning techniques have been widely used in the reality that it is
difficult to obtain sufficient labeled data in the target domain, but a large
amount of auxiliary data can be obtained in the relevant source domain. But
most of the existing methods are based on offline data. In practical
applications, it is often necessary to face online learning problems in which
the data samples are achieved sequentially. In this paper, We are committed to
applying the ensemble approach to solving the problem of online transfer
learning so that it can be used in anytime setting. More specifically, we
propose a novel online transfer learning framework, which applies the idea of
online bagging methods to anytime transfer learning problems, and constructs
strong classifiers through online iterations of the usefulness of multiple weak
classifiers. Further, our algorithm also provides two extension schemes to
reduce the impact of negative transfer. Experiments on three real data sets
show that the effectiveness of our proposed algorithms.
