## A `Microscopic' Study of Minimum Entropy Search in Learning Decomposable Markov Networks (1995)

Venue: | MACHINE LEARNING |

Citations: | 23 - 18 self |

### BibTeX

@INPROCEEDINGS{Xiang95a`microscopic',

author = {Y. Xiang and S.K.M. Wong and N. Cercone},

title = {A `Microscopic' Study of Minimum Entropy Search in Learning Decomposable Markov Networks},

booktitle = {MACHINE LEARNING},

year = {1995},

publisher = {}

}

### OpenURL

### Abstract

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.