.png){kind=icon}
返回
An interactive approach for Bayesian network learning using domain/expert knowledge
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijar.2013.03.009.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Using domain/expert knowledge when learning Bayesian networks from data has been considered a promising idea since the very beginning of the field. However, in most of the previously proposed approaches, human experts do not play an active role in the learning process. Once their knowledge is elicited, they do not participate any more. The interactive approach for integrating domain/expert knowledge we propose in this work aims to be more efficient and effective. In contrast to previous approaches, our method performs an active interaction with the expert in order to guide the search based learning process. This method relies on identifying the edges of the graph structure which are more unreliable considering the information present in the learning data. Another contribution of our approach is the integration of domain/expert knowledge at different stages of the learning process of a Bayesian network: while learning the skeleton and when directing the edges of the directed acyclic graph structure. (C) 2013 Elsevier Inc. All rights reserved.
Keyword:
Probabilistic graphical models
Bayesian networks
Interactive structure learning
Domain expert knowledge
Stochastic search
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
3.0K
被引数:
5.1K
机构
引用论文
分享收藏Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks关于网络结构的贝叶斯。贝叶斯网络中结构发现的贝叶斯方法.png)
MACHINE LEARNINGOAAI
MACHINE LEARNINGIF2.9
OAAI分享收藏
查看更多内容