Tech Reports
Tech Report kmi-98-05 Abstract
Learning Conditional Probabilities from Incomplete Data: An Experimental Comparison
Techreport ID: kmi-98-05
Date: 1998
Author(s): Marco Ramoni and Paola Sebastiani
This paper reports some experimental results comparing three parametric methods, Gibbs Sampling, EM algorithm and Bound and Collapse, for the estimation of conditional probability distributions from incomplete databases. 1. Knowledge Media Institute, The Open University. 2. Department of Actuarial Science and Statistics, City University.
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KnowledgeManagementMultimedia &
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HypermediaNew Media SystemsSemantic Web &
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We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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