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-  2015 

偏标记学习研究综述
Research on Partial Label Learning

DOI: 10.16337/j.1004-9037.2015.01.007

Keywords: 机器学习,弱监督信息,偏标记学习,候选标记,纠错输出编码
machine learning
, weakly-supervised information, partial label learning, candidate label, error-correcting output codes (ECOC)

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Abstract:

在弱监督信息条件下进行学习已成为机器学习领域的热点研究课题。偏标记学习作为一类重要的弱监督机器学习框架,适于多种实际应用问题的学习建模。在该框架下,每个对象在输入空间由单个示例(属性向量)进行刻画,而在输出空间与一组候选标记相关联,其中仅有一个为其真实标记。本文将对偏标记学习的研究现 状进行综述,首先给出该学习框架的定义以及与相关学习框架的区别与联系,然后重点介绍几种典型的偏标记学习算法以及作者在该方面的初步工作,最后对偏标记学习进一步的研究方向进行简要讨论。
In recent years, learning with weak supervision has become one of the hot research topics in machine learning. As one of the important weakly-supervised machine learning frameworks, partial label learning has been successfully applied to a number of real-world applications. In partial label learning, each object is described by a single instance (feature vector) in the input space. On the other hand, it is associated with a set of candidate labels among which only one is valid. The state-of-the-art on partial label learning researches is reviewed. Firstly, the problem definition on partial label learning as well as its differences and similarities with other related learning frameworks are given. Thenseveral representative partial label learning algorithms along with one of our recent progress on this topic are introduced. Finally, possible future investigations on partial label learning are briefly discussed.

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