计算机科学 ›› 2012, Vol. 39 ›› Issue (Z6): 635-539.

• • 上一篇    

一种针对多关系数据的半监督协同训练算法

王 娇,罗四维,王 立   

  1. (中央广播电视大学计算机科学与技术系 北京100031);(北京交通大学计算机应用研究所 北京100044)
  • 出版日期:2018-11-16 发布日期:2018-11-16

New Co-training Algorithm for Multi-relation Data

  • Online:2018-11-16 Published:2018-11-16

摘要: 半监督学习是机器学习领域的研究热点。协同训练研究数据有多个特征集时的半监督学习问题。将图表示法引入协同训练,使用多个图结构表示多关系数据。在每个图上进行半监督学习,在多个图之间进行协同学习,使多个图上的学习器对数据的预测一致。创新性地提出一种针对多关系数据的半监督协同训练算法,并从概率角度分析学习过程。在真实数据集上的实验表明,提出的算法处理多关系数据时具有较好的性能。

关键词: 机器学习,半监督学习,协同训练,多关系数据

Abstract: Semi-supervised learning is a hot research topic of machine learning. Co-training is a multi-view semi-supervised learning method. Uraph representation was introduced to co-training where multiple graphs were used to represent multi-relation data. Semi-supervised learning was processed on each graph while co-training was conducted between graphs to ensure the predictions of graphs arc the same. A new co-training algorithm for multi-relation data was proposed,and it was analyzed from the viewpoint of probability. Encouraging experimental results are gotten from real world multi-relation dataset.

Key words: Machine learning, Semi-supervised learning, Co-training, Multi-relation data

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