Concepts from Time Series

This paper describes a wayof 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. Theseclusters, in turn, represent perceptual categories with predictive value to the agent/environment system. Wealso discuss the relationship betweencategories and concepts, with particular emphasis on class membership and predictive inference.

Authors

• Michael T. Rosenstein Andrew H. Fagg

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