Active learning with adaptive regularization

In classification problems, active learning is often adopted to alleviate the laborious human labeling efforts, by finding the most informative samples to query the labels. One of the most popular query strategy is selecting the most uncertain samples for the current classifier. The performance of such an active learning process heavily relies on the learned classifier before each query. Thus, stepwise classifier model/parameter selection is quite critical, which is, however, rarely studied in the literature. In this paper, we propose a novel active learning support vector machine algorithm with adaptive model selection. In this algorithm, before each new query, we trace the full solution path of the base classifier, and then perform efficient model selection using the unlabeled samples. This strategy significantly improves the active learning efficiency with comparatively inexpensive computational cost. Empirical results on both artificial and real world benchmark data sets show the encouraging gains brought by the proposed algorithm in terms of both classification accuracy and computational cost.