Tech Reports
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
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
Publication(s):
Also in Sequence Learning: Paradigms, Algorithms, and Applications, L. Giles and R. Sun, editors, Springer, New York, NY, 2000.
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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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