Concepts from Time Series

Michael T. Rosenstein, Paul R. Cohen

This paper describes a way of extracting concepts from streams of sensor readings. In particular, we demonstrate the value of attractor reconstruction techniques for transforming time series into clusters of points. These clusters, in turn, represent perceptual categories with predictive value to the agent/environment system. We also discuss the relationship between categories and concepts, with particular emphasis on class membership and predictive inference.


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