摘要： 在分类器训练过程中，无标记数据的引入容易产生噪音，从而降低分类精度。为此，提出一种基于图的置信度估计半监督协同训练算法。利用样本数据自身的结构信息，计算无标记样本所属类别概率。采用多分类器对无标记数据进行置信度估计，以提高无标记数据挑选标准，减少噪音数据的引入。在UCI数据集上的对比实验验证了该算法的有效性。

关键词: 半监督学习, 协同训练, 置信度, 分类, 无标记数据

Abstract: In classifier training process, the introduction of unlabeled data can cause noise data, and it reduces classification accuracy. This paper proposes Confidence Estimation for Semi-supervised Learning based on graph(CESL) algorithm. The algorithm makes use of structure information of sample data to calculate classification probability of unlabeled data explicitly. Combined with multi-classifiers, the algorithm estimates the confidence of unlabeled data implicitly and improves the selection criteria. With dual-confidence estimation, the unlabeled data is selected to update classifiers. Experiments on UCI datasets prove the efficiency of this algorithm.

Key words: semi-supervised learning, collaborative training, confidence, classification, unlabeled data

中图分类号: 

• TP312