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  • 1
    Electronic Resource
    Electronic Resource
    A “Microscopic” Study of Minimum Entropy Search in Learning Decomposable Markov Networks (1997)
    Springer
    Machine learning 26 (1997), S. 65-92 
    Details
    ISSN: 0885-6125
    Keywords: inductive learning ; reasoning under uncertainty ; knowledge acquisition ; Markov networks ; probabilistic networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: 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.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1007324100110
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    An Extended Relational Data Model For Probabilistic Reasoning (1997)
    Springer
    Journal of intelligent information systems 9 (1997), S. 181-202 
    Details
    ISSN: 1573-7675
    Keywords: Relational database ; probabilistic reasoning ; knowledge representation ; generalized acyclic join dependency ; belief networks
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Abstract Probabilistic methods provide a formalism for reasoning aboutpartial beliefs under conditions of uncertainty. This paper suggests a newrepresentation of probabilistic knowledge. This representation encompassesthe traditional relational database model. In particular, it is shown thatprobabilistic conditional independence is equivalent to the notion of generalized multivalued dependency. More importantly,a Markov network can be viewed as a generalized acyclic joindependency. This linkage between these two apparently different butclosely related knowledge representations provides a foundation fordeveloping a unified model for probabilistic reasoning and relationaldatabase systems.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1023/A:1008603515938
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
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