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引用次数: 4
摘要
在许多学习任务中,存在大量未标记的样本,但标记的训练样本数量有限,因为标记样本需要人类注释者的努力和专业知识。有三种标记样本的主要技术:半监督学习、转导学习和主动学习。除了提高学习性能外,半监督和转导学习还涉及自动开发未标记样本的方法。主动学习处理的方法假设学习者控制整个输入空间。因此,结合半监督学习和主动学习的优点是提高学习性能的一种实用技术。本文提出了一种结合(主动学习)人工智能和(半监督学习)SSL算法的通用框架。然后介绍了结合ai和SSL算法的集成学习,用ASC (AL and SSL by Committee)表示。最后讨论了ASC的集成学习和置信度度量。
Combining active learning and semi-supervised for improving learning performance
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.