Concepts from Time Series
Abstract
This paper describes a way of extracting concepts from streams of sensor readings. In particular, we demon-strate 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 member-ship and predictive inference.
Cite
Text
Rosenstein and Cohen. "Concepts from Time Series." AAAI Conference on Artificial Intelligence, 1998.Markdown
[Rosenstein and Cohen. "Concepts from Time Series." AAAI Conference on Artificial Intelligence, 1998.](https://mlanthology.org/aaai/1998/rosenstein1998aaai-concepts/)BibTeX
@inproceedings{rosenstein1998aaai-concepts,
title = {{Concepts from Time Series}},
author = {Rosenstein, Michael T. and Cohen, Paul R.},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {1998},
pages = {739-745},
url = {https://mlanthology.org/aaai/1998/rosenstein1998aaai-concepts/}
}