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Transfer learning techniques have been widely used in the reality that it isdifficult to obtain sufficient labeled data in the target domain, but a largeamount of auxiliary data can be obtained in the relevant source domain. Butmost of the existing methods are based on offline data. In practicalapplications, it is often necessary to face online learning problems in whichthe data samples are achieved sequentially. In this paper, We are committed toapplying the ensemble approach to solving the problem of online transferlearning so that it can be used in anytime setting. More specifically, wepropose a novel online transfer learning framework, which applies the idea ofonline bagging methods to anytime transfer learning problems, and constructsstrong classifiers through online iterations of the usefulness of multiple weakclassifiers. Further, our algorithm also provides two extension schemes toreduce the impact of negative transfer. Experiments on three real data setsshow that the effectiveness of our proposed algorithms.

Online Bagging for Anytime Transfer Learning