Graph-incorporated Active Learning with SVM

摘要

Active learning is typically limited by the small sample problem which makes the resulting classifiers perform poorly, especially in the initial stages. To overcome this problem, in this paper, a novel framework - graph-incorporated active learning - is proposed, in which the selection pool is regarded as a graph. Its graph structure is applied to both improve sample selection criterion and provide the learner enough pseudo-labeled samples. By comparing with the state-of-the-art technique, the experiments on benchmark datasets show that the improvement of the proposed method is significant, i.e., it can solve the small problem well. The framework is combined with, but is not limited to, SVM.