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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Interacting with other people not only forms the core of human social and psychological experience, but also lies at the centre of what makes the internet such a rich, powerful and exciting collection of knowledge media. We are especially interested in what happens when such interactions take place on a very large scale -- not only because we work regularly with tens of thousands of distance learners at the Open University, but also because it is evident that being part of a crowd in real life possesses a certain 'buzz' of its own, and poses a natural challenge. Different nuances emerge in different user contexts, so we choose to investigate the contexts of work, learning and play to better understand the trade-offs involved in designing effective large-scale social software for multiple purposes.
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