A ”microscopic” study of minimum entropy search in learning decomposable Markov networks. (English) Zbl 0866.68088

Xiang, Y.; Wong, S. K. M.; Cercone, N.

Mach. Learn. 26, No. 1, 65-92 (1997).

Summary: Several scoring metrics are used in different search procedures for learning probabilistic networks. We study the properties of cross entropy in learning a decomposable Markov network. Though entropy and related scoring metrics were widely used, its ‘microscopic’ properties and asymptotic behavior in a search have not been analyzed. We present such a ‘microscopic’ study of a minimum entropy search algorithm, and show that it learns an \(I\)-map of the domain model when the data size is large.
Search procedures that modify a network structure one link at a time have been commonly used for efficiency. Our study indicates that a class of domain models cannot be learned by such procedures. This suggests that prior knowledge about the problem domain together with a multi-link search strategy would provide an effective way to uncover many domain models.

MSC:

68T05

Learning and adaptive systems in artificial intelligence

Keywords:

probabilistic networks