  We study a pool-based sequential active learning (AL), in which one sample is
queried at each time from a large pool of unlabeled data according to a
selection criterion. For this framework, we propose two selection criteria,
named expected-kernel-discrepancy (EKD) and expected-kernel-loss (EKL), by
leveraging the particular structure of multiple kernel learning (MKL). Also, it
is identified that the proposed EKD and EKL successfully generalize the
concepts of popular query-by-committee (QBC) and expected-model-change (EMC),
respectively. Via experimental results with real-data sets, we verify the
effectiveness of the proposed criteria compared with the existing methods.
