第 9 页 特 邀 报 告(五) 题目:多标记学习的研究 摘 要:多标记学习(Multi-Label Learning)是一种新型多义性对象建模框架,在 该框架下,每个对象由一个示例描述且同时具有多个类别标记。
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This paper proposes a new algorithm DTML(Determination Threshold for Multi-label Learning). An optimal threshold is set for each label by learning from the training data set.
提出一种多标记学习阈值确定算法(DTML),为每个类别标记确定一个阈值。
参考来源 - 用于多标记学习的阈值确定算法·2,447,543篇论文数据,部分数据来源于NoteExpress
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。
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