利用节点顺序置信指导增量学习贝叶斯网络的研究与应用

摘要

By introducing the node order of confidence in the procedure of incremental learning,so the NOCLBN algorithm is proposed.For the learning procedure of Bayesian network under large-scale data set,the algorithm enhances the accuracy of the study of each batch of data,thus improving the quality of the final network model.Experimental results show that the NOCLBN algorithm can obtain high quality for the learning results of Bayesian network under large-scale data set.%将节点顺序置信指导的方法融入到增量学习过程中,提出了NOCLBN算法.该算法对于大规模数据集下贝叶斯网络的学习过程进行了改进,增强了每一批次数据学习的精度,提高了最终网络模型的质量.实验结果表明,NOCLBN算法对于大规模数据集下贝叶斯网络学习的结果质量更高.