A `Microscopic' Study of Minimum Entropy Search in Learning Decomposable Markov Networks (1995)
| Venue: | MACHINE LEARNING |
| Citations: | 17 - 12 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 = {}
}
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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.
