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Sequence Learning via Bayesian Clustering by Dynamics
| Book Series | Lecture Notes in Computer Science |
| Publisher | Springer Berlin / Heidelberg |
| ISSN | 0302-9743 (Print) 1611-3349 (Online) |
| Volume | Volume 1828/2001 |
| Book | Sequence Learning |
| DOI | 10.1007/3-540-44565-X |
| Copyright | 2001 |
| ISBN | 978-3-540-41597-8 |
| DOI | 10.1007/3-540-44565-X_2 |
| Pages | 11-34 |
| Subject Collection | Computer Science |
| SpringerLink Date | Monday, January 01, 2001 |
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Sequence Learning via Bayesian Clustering by Dynamics
Paola Sebastiani3, Marco Ramoni4 and Paul Cohen5
| (3) |
Department of Mathematics and Statistics, University of Massachusetts, Amherst |
| (4) |
Knowledge Media Institute, The Open University, UK |
| (5) |
Department of Computer Science, University of Massachusetts, Amherst |
Abstract
Suppose one has a set of univariate time series generated by one or more unknown processes. The problem we wish to solve is
to discover the most probable set of processes generating the data by clustering time series into groups so that the elements
of each group have similar dynamics. For example, if a batch of time series represents sensory experiences of a mobile robot,
clustering by dynamics might find clusters corresponding to abstractions of sensory inputs (Ramoni, Sebastiani, Cohen, Warwick, & Davis, 1999).
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