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Representing Learning With Graphical ModelsProbabilistic graphical models are being used widely in artificial intelligence, for instance, in diagnosis and expert systems, as a unified qualitative and quantitative framework for representing and reasoning with probabilities and independencies. Their development and use spans several fields including artificial intelligence, decision theory and statistics, and provides an important bridge between these communities. This paper shows by way of example that these models can be extended to machine learning, neural networks and knowledge discovery by representing the notion of a sample on the graphical model. Not only does this allow a flexible variety of learning problems to be represented, it also provides the means for representing the goal of learning and opens the way for the automatic development of learning algorithms from specifications.
Document ID
20020006313
Acquisition Source
Ames Research Center
Document Type
Preprint (Draft being sent to journal)
Authors
Buntine, Wray L.
(Research Inst. for Advanced Computer Science Moffett Field, CA United States)
Lum, Henry, Jr.
Date Acquired
August 20, 2013
Publication Date
January 1, 1994
Subject Category
Cybernetics, Artificial Intelligence And Robotics
Funding Number(s)
PROJECT: RTOP 233-01-02
Distribution Limits
Public
Copyright
Work of the US Gov. Public Use Permitted.

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