  In many real-world applications, data are often collected in the form of
stream, and thus the distribution usually changes in nature, which is referred
as concept drift in literature. We propose a novel and effective approach to
handle concept drift via model reuse, leveraging previous knowledge by reusing
models. Each model is associated with a weight representing its reusability
towards current data, and the weight is adaptively adjusted according to the
model performance. We provide generalization and regret analysis. Experimental
results also validate the superiority of our approach on both synthetic and
real-world datasets.
