Active learning based on support vector machines

Active learning is mainly to select a part of unlabelled samples from a big dataset. The selected samples are then submitted to domain experts to label and added to the training set. Suppose that the price of labeling samples is far more than the computational cost of training algorithms, we propose a scheme of active learning based on support vector machines, which follows the traditionally inductive learning model of general-specific. In terms of the number of selected samples, the training cost, and the generalization ability, a comparison with some existing active learning algorithms is conducted. The advantages and disadvantages are demonstrated experimentally.