Multi-label learning is a common problem in real application. Covering algorithm performs well with single-label learning but can not deal with multi-label learning.
多标记学习是实际应用中的一类常见问题，覆盖算法在单标记学习中表现出了优秀的性能，但无法处理多标记情况。
Real-world text documents usually belong to multiple classes simultaneously, and therefore, using multi-label learning technique to classify text documents is an important research direction.
真实世界的文档往往同时属于多个类别，因此，利用多标记学习技术进行文档分类是一个重要的研究方向。
Aiming at the problems that active learning in multi-label classification is slowly, this paper proposes an improved method for multi-label classification which based on average expectation margin.
针对多标签主动学习速度较慢的问题，提出一种基于平均期望间隔的多标签分类的主动学习方法。
Aiming at the problems that active learning in multi-label classification is slowly, this paper proposes an improved method for multi-label classification which based on average expectation margin.
针对多标签主动学习速度较慢的问题，提出一种基于平均期望间隔的多标签分类的主动学习方法。
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