Combining active learning and semi-supervised for improving learning performance

Abstract

In many learning tasks, there are abundant unlabeled samples but the number of labeled training samples is limited, because labeling the samples requires the efforts of human annotators and expertise. There are three major techniques for labeling the samples: semi-supervised learning, transductive learning and active learning. Semi-supervised and transductive learning deal with methods for automated exploiting unlabeled samples in addition to improve learning performance. Active learning deals with methods that assume the learner has control over the whole input space. So combing the advantage of semi-supervised learning and active learning is a practical technique for improving the learning performance. In this paper, a general framework of combing (Active Learning) AL and (Semi-Supervised Learning) SSL algorithms is proposed. Then the ensemble learning for combing AL and SSL algorithms is introduced, which is denoted by ASC (AL and SSL by Committee). At last, the ensemble learning and confidence measure of the ASC is discussed.