摘要
基于图的半监督学习方法中,图结构经常要预先设定,这就导致了在标签传递过程中,算法不能自适应地学习一个最优的图.为此,提出了一种基于自适应图的半监督学习方法.该方法通过迭代的优化方法同时学习到最优的图和标签.而且,在少量标记样本的情况下该方法也可以得到较高的分类准确率,并通过实验证明了该方法的有效性.
In most graph-based semi-supervised methods, graph structure is often set in advance, which leads to the fact that the algorithm can＇t learn an optimal graph in the process of label propagation. Therefore, this paper proposes a method called Adaptive Graph-based Semi-supervised Learning Method （AGSSLM）. This method can leam the optimal graph and label simultaneously by using the iterative optimization method. Moreover, this method can also obtain higher classification accuracy with fewer labeled samples. The experimental results validate the effectiveness of this method.
出处
《计算机系统应用》
2014年第2期173-177,共5页
Computer Systems & Applications
关键词
半监督学习
标签传递
优化算法
分类准确率
semi-supervised learning
label Propagation
optimization algorithm
classification accuracy
