论文标题
可变聚类和压缩的方法，用于学习大型贝叶斯网络
Approach of variable clustering and compression for learning large Bayesian networks
论文作者
论文摘要
本文介绍了一种基于特征空间群集产生的块来学习大贝叶斯网络结构的新方法。使用归一化的共同信息获得此聚类。随后的块聚集是使用经典学习方法完成的，除了它们是输入的，其中包含有关每个块特征值组合的压缩信息。该方法的验证是针对爬山的两种分数函数的图表枚举算法进行的：BIC和MI。这样，即使对于那些被认为不适合并行学习的分数函数，也可以实现潜在的可行块学习。该方法的优势是根据工作速度以及发现结构的准确性评估的。
This paper describes a new approach for learning structures of large Bayesian networks based on blocks resulting from feature space clustering. This clustering is obtained using normalized mutual information. And the subsequent aggregation of blocks is done using classical learning methods except that they are input with compressed information about combinations of feature values for each block. Validation of this approach is done for Hill-Climbing as a graph enumeration algorithm for two score functions: BIC and MI. In this way, potentially parallelizable block learning can be implemented even for those score functions that are considered unsuitable for parallelizable learning. The advantage of the approach is evaluated in terms of speed of work as well as the accuracy of the found structures.
