摘要
针对贝叶斯网络参数迁移过程中对源域及目标域限定条件较多等问题,在考虑源域-目标域多种信息形式的情况下,提出一种基于贝叶斯网络参数迁移学习的统一框架.该方法综合考虑了源域结构和数据量在迁移中的作用,在结构相似性的基础上,探讨了备选源域数据量对参数迁移的影响.在迁移过程中引入与目标域数据相关的平衡系数.通过平衡系数将目标域数据与迁移过程联系起来,实现平衡系数的自动调节.Asia网络验证了本文方法的准确性.
In order to solve the problem that there are many restrictions on the source domain and the target domain in the process of Bayesian network parameter transfer,a unified framework based on Bayesian network parameter transfer learning was proposed under the condition of considering multiple information forms of source domain and target domain.The method considers the role of source domain structure and data volume in the migration.On the basis of structural similarity,the influence of alternative source domain data volume on parameter migration was discussed.The balance coefficient related to the target domain data was introduced in the migration process.According to the balance coefficient,the target domain data was linked with the migration process to realize the automatic adjustment of the balance coefficient.The Asia network verifies the accuracy of the method in this paper.
作者
王姝
关展旭
王晶
孙晓辉
WANG Shu;GUAN Zhan-xu;WANG Jing;SUN Xiao-hui(School of Information Science&Engineering,Northeastern University,Shenyang 110819,China;Dalian Tianlai Security Risk Management Technology Limited Company,Dalian 116021,China)
出处
《东北大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第4期509-515,共7页
Journal of Northeastern University(Natural Science)
基金
国家自然科学基金资助项目(61973057)
矿冶过程自动控制技术国家(北京市)重点实验室开放课题(BGRIMM-KZSKL-2018-09).
关键词
贝叶斯网络
参数学习
迁移学习
结构相似性
平衡系数
Bayesian network
parameter learning
transfer learning
structural similarity
equilibrium coefficient
