摘要
贝叶斯网络作为不确定性知识表达和推理的一种方法在很多领域都有着广泛的应用,作者在文中提出了一种根据许多专家提供的规则库进行贝叶斯网络结构学习的新算法,并且通过严密的推理对以往的CPT学习算法进行了一些有意义的改进,进而形成了一个较为完备的贝叶斯网络学习。
As a method of describing uncertainly knowledge and reasoning , Bayesian network is applied extensively in many fields. This thesis analyses many kinds of algorithm about Bayesian network structure learning, and then setting-up a new Algorithm about structure learning , the thesis has a meaningful change in CPT learning through tight reasoning
出处
《陕西科技大学学报（自然科学版）》
2005年第2期93-96,共4页
Journal of Shaanxi University of Science & Technology
