学习信度网的结构

Learning Network Structure From Data

一、等价的信度网结构学习信度网的结构,就是通过分析实例数据库,建立能够表达实例数据所包含信息的信度网的结构。任何一个由所有的结点(变量)构成的有向无环图都可能作为信度网的结构。如对图1(d)所示的关于吸烟、性别及肺癌的实例数据库。

A general approach to learn a network structure is to heuristically search the space of network structures for the one that best fits a given data set. The key to the search is a score function which evaluates different network structures. In this paper,we give a detailed introduction to two representative score functions: BDe, MDL- We also discuss two widely used learning algorithms that apply those score functions to direct their search: hill climbing and simulated annealing. Finally, We briefly sketch an algorithm,SEM ,that can learn a network structure from incomplete data.