Concepts from time series

Michael T. Rosenstein; Paul R. Cohen

Concepts from time series

Michael T. Rosenstein, Paul R. Cohen

Research output: Contribution to conference › Paper › peer-review

Abstract

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.

Original language	English (US)
Pages	739-745
Number of pages	7
State	Published - 1998
Externally published	Yes
Event	Proceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI - Madison, WI, USA Duration: Jul 26 1998 → Jul 30 1998

Other

Other	Proceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI
City	Madison, WI, USA
Period	7/26/98 → 7/30/98

ASJC Scopus subject areas

Software
Artificial Intelligence

Cite this

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title = "Concepts from time series",

abstract = "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.",

author = "Rosenstein, {Michael T.} and Cohen, {Paul R.}",

year = "1998",

language = "English (US)",

pages = "739--745",

note = "Proceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI ; Conference date: 26-07-1998 Through 30-07-1998",

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TY - CONF

T1 - Concepts from time series

AU - Rosenstein, Michael T.

AU - Cohen, Paul R.

PY - 1998

Y1 - 1998

N2 - 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.

AB - 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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M3 - Paper

AN - SCOPUS:0031644076

SP - 739

EP - 745

T2 - Proceedings of the 1998 15th National Conference on Artificial Intelligence, AAAI

Y2 - 26 July 1998 through 30 July 1998

ER -

Concepts from time series

Abstract

Other

ASJC Scopus subject areas

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