摘要
解决偏标记问题的基本策略是消歧,现有的消歧策略大都分别对每个示例单独进行消歧,并未充分利用示例之间的相关性.基于此原因,文中提出一致性偏标记学习算法(COPAL).该算法基于一个基本假设:相似示例的标记也应该有相关性.基于该假设,COPAL在消歧过程中同时考虑样本自身及其近邻样本的标记信息.实验表明,在人工合成的UCI数据集和真实数据集上,COPAL均取得较好的泛化性能.
An essential strategy to solve the partial label problem is disambiguation. In most existing strategies, instances are individually disambiguated without the consideration of the relationships among instances. In this paper, a consistency based partial label learning （COPAL） algorithm is proposed assumpting that labels associated with similar instances are likely to be similar. Based on the above assumption, the labeling information of the instance itself and its neighboring instances are simultaneously utilized for disambiguation. Experiments on both artificial datasets and realworld datasets show the good generalization ability of COPAL.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2016年第12期1140-1146,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.61573104
61222309)
教育部新世纪优秀人才支持计划(No.NCET-13-0130)资助~~
关键词
偏标记学习
一致性
弱监督学习
消歧策略
Partial Label Learning, Consistency, Weakly Supervised Learning, Disambiguation Strategy
