Tech Reports
Tech Report kmi-99-06 Abstract
An Introduction to the Robust Bayesian Classifier
Techreport ID: kmi-99-06
Date: 1999
Author(s): Marco Ramoni and Paola Sebastiani
Bayesian supervised classifiers are one of the most promising data mining techniques for large scale applications. When the database is complete, they provide an efficient and scalable solution to classification problems. When some data are missing in the training set, methods exist to learn these classifiers, albeit less efficiently, under the assumption that data are missing at random. This paper describes the implementation of RoC, a Bayesian classifier able handle incomplete databases with no assumption about the pattern of missing data. 1. Knowledge Media Institute, The Open University 2. Department of Statistics, The Open 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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