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Tech Report kmi-00-05 Abstract


Sequence Learning via Bayesian Clustering by Dynamics
Techreport ID: kmi-00-05
Date: 2000
Author(s): Paola Sebastiani, Marco Ramoni and Paul Cohen
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This chapter introduces a Bayesian method for clustering dynamic processes. The method models dynamics as Markov chains and then applies an agglomerative clustering procedure to discover the most probable set of clusters capturing different dynamics. To increase efficiency, the algorithm uses an entropy-based heuristic search strategy. When applied to clustering sensor data from mobile robots, the algorithm produces clusters that are meaningful in the domains of application. 1. Department of Mathematics, Imperial College of Science, Technology and Medicine 2. Knowledge Media Institute, The Open University 3. Department of Computer Science, University of Massachusetts at Amherst

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Also in Sequence Learning: Paradigms, Algorithms, and Applications, L. Giles and R. Sun, editors, Springer, New York, NY, 2000.
 
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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.