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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
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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
 
KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Knowledge Management is...


Knowledge Management
Creating learning organisations hinges on managing knowledge at many levels. Knowledge can be provided by individuals or it can be created as a collective effort of a group working together towards a common goal, it can be situated as "war stories" or it can be generalised as guidelines, it can be described informally as comments in a natural language, pictures and technical drawings or it can be formalised as mathematical formulae and rules, it can be expressed explicitly or it can be tacit, embedded in the work product. The recipient of knowledge - the learner - can be an individual or a work group, professionals, university students, schoolchildren or informal communities of interest.
Our aim is to capture, analyse and organise knowledge, regardless of its origin and form and make it available to the learner when needed presented with the necessary context and in a form supporting the learning processes.