| 摘要: |
| 针对传统算法分类速度较慢的不足,改进传统算法中候选变量的搜索方式,提出用依赖度量函数测量变量之间的依赖程度,得出压缩候选的贝叶斯信念网络构造算法.该算法在不影响原有算法可靠性的前提下,提高了学习速度. |
| 关键词: 贝叶斯信念网络 压缩候选 算法 数据挖掘 |
| DOI： |
| 投稿时间：2005-05-19修订日期：2005-08-05 |
| 基金项目: |
|
| Algorithm of Bayesian Belief Network Structure of Compressed Candidature |
|
Yang Benliang
|
| (Wuzhou Business Bureau of Guangxi, Wuzhou, Guangxi, 543000, China) |
| Abstract: |
| A learning algorithm of compressed candidates based on Bayesia belief network is developed to solve slow running problem of traditional Bayesian belief network constructing algorithm.The improved method for searching candidates with a modified dependence measure is used in the presented algorithm which can speed up the study process without sacrificing the reliability of the traditional method. |
| Key words: Bayesian belief network compressed candidature algorithm data mining |
