项目名称: 标记分布学习
项目编号: No.61273300
项目类型: 面上项目
立项/批准年度: 2013
项目学科: 自动化技术、计算机技术
项目作者: 耿新
作者单位: 东南大学
项目金额: 81万元
中文摘要: 对一个特定的应用来说,一个多义性对象不同含义的重要性往往是不同的,而现有的多标记学习框架不能很好的匹配这一现象。标记分布学习是为此提出的一种新型学习框架。在该框架内,一个示例不是由一个标记集,而是由一个标记分布来标注。标记分布中对应每个标记都有一个表示其重要性的实数值,称为描述度。标记分布学习相较多标记学习展现出更多的一般性、灵活性和表达力,是解决多义性问题的一种崭新尝试。并且由于单标记和多标记学习都可以看作标记分布学习的特例,对标记分布学习的研究也有可能对解决单标记和多标记学习中的问题起到启发和促进作用。本项目是对标记分布学习最初的探索之一,将研究其基础理论、算法、应用,以及其在群学习和利用标记间相关性方面的作用。本项目预期将建立一个标记分布学习基本理论框架,提出若干标记分布学习算法,并通过实际应用证明其价值。预期成果包括8-10篇高质量论文、2-3项发明专利,以及培养6-8名研究生。
中文关键词: 机器学习;标记分布学习;;;
英文摘要: In a particular application, the importance of the different meanings of an ambiguous object is often different. The current framework of multi-label learning cannot match this phenomenon well. To deal with such situation, label distribution learning is proposed as a new learning framework, where an instance is not labeled by a label set, but by a label distribution. For each label in a label distribution, there is a real number called description degree, which represents the importance of the corresponding label. Compared with multi-label learning, label distribution learning is more general, flexible, and expressive. It is a fresh try to solve the ambiguity problem in learning. Moreover, both single-label and multi-label learning can be viewed as special cases of label distribution learning. Thus the research on label distribution learning might help to solve the problems in single-label and multi-label learning. This project is one of the first explorations of label distribution learning, which will study its theory, algorithms, applications, and uses in learning from crowds and the utilization of label correlation. This project endeavors to propose a basic theoretical framework and several algorithms for label distribution learning, and prove their values in real applications. It will result in 8-10 high-qua
英文关键词: Machine Learning;Label Distribution Learning;;;