ISSN:
0885-6125
Schlagwort(e):
inductive learning
;
reasoning under uncertainty
;
knowledge acquisition
;
Markov networks
;
probabilistic networks
Quelle:
Springer Online Journal Archives 1860-2000
Thema:
Informatik
Notizen:
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.
Materialart:
Digitale Medien
URL:
http://dx.doi.org/10.1023/A:1007324100110
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