Some dimensions of problem complexity for XCS

Tim Kovacs; Kerber Manfred; Wu Annie S.

Some dimensions of problem complexity for XCS

Tim Kovacs
, Kerber Manfred
, Wu Annie S.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Despite two decades of work, learning classifier systems researchers have had relatively little to say on the subject of what makes a problem difficult for a classifier system. One focus of our work has been the issue of what makes a problem difficult for XCS -- Wilson's recent accuracy-based classifier system. This document outlines the approach taken, provides some initial results and outlines possible directions for future work. [This work is subsumed by "What makes a problem hard for XCS?". A chapter in Advances in Learning Classifier Systems. P. L. Lanzi, W. Stolzmann, S. W. Wilson, (eds.), pp. 80–99. April 2001]

Translated title of the contribution	Some dimensions of problem complexity for XCS
Original language	English
Title of host publication	Proceedings of the GECCO-2000 Graduate Student Workshop
Publication status	Published - 2000

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Conference Proceedings/Title of Journal: Proceedings of the GECCO-2000 Graduate Student Workshop
Other identifier: 2001080

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@inproceedings{2f60ec07e9db4575b511371704acca10,

title = "Some dimensions of problem complexity for XCS",

abstract = "Despite two decades of work, learning classifier systems researchers have had relatively little to say on the subject of what makes a problem difficult for a classifier system. One focus of our work has been the issue of what makes a problem difficult for XCS -- Wilson's recent accuracy-based classifier system. This document outlines the approach taken, provides some initial results and outlines possible directions for future work. [This work is subsumed by {"}What makes a problem hard for XCS?{"}. A chapter in Advances in Learning Classifier Systems. P. L. Lanzi, W. Stolzmann, S. W. Wilson, (eds.), pp. 80–99. April 2001] ",

author = "Tim Kovacs and Kerber Manfred and \{Annie S.\}, Wu",

note = "Other page information: - Conference Proceedings/Title of Journal: Proceedings of the GECCO-2000 Graduate Student Workshop Other identifier: 2001080",

year = "2000",

language = "English",

booktitle = "Proceedings of the GECCO-2000 Graduate Student Workshop",

}

TY - GEN

T1 - Some dimensions of problem complexity for XCS

AU - Kovacs, Tim

AU - Manfred, Kerber

AU - Annie S., Wu

N1 - Other page information: - Conference Proceedings/Title of Journal: Proceedings of the GECCO-2000 Graduate Student Workshop Other identifier: 2001080

PY - 2000

Y1 - 2000

N2 - Despite two decades of work, learning classifier systems researchers have had relatively little to say on the subject of what makes a problem difficult for a classifier system. One focus of our work has been the issue of what makes a problem difficult for XCS -- Wilson's recent accuracy-based classifier system. This document outlines the approach taken, provides some initial results and outlines possible directions for future work. [This work is subsumed by "What makes a problem hard for XCS?". A chapter in Advances in Learning Classifier Systems. P. L. Lanzi, W. Stolzmann, S. W. Wilson, (eds.), pp. 80–99. April 2001]

AB - Despite two decades of work, learning classifier systems researchers have had relatively little to say on the subject of what makes a problem difficult for a classifier system. One focus of our work has been the issue of what makes a problem difficult for XCS -- Wilson's recent accuracy-based classifier system. This document outlines the approach taken, provides some initial results and outlines possible directions for future work. [This work is subsumed by "What makes a problem hard for XCS?". A chapter in Advances in Learning Classifier Systems. P. L. Lanzi, W. Stolzmann, S. W. Wilson, (eds.), pp. 80–99. April 2001]

M3 - Conference Contribution (Conference Proceeding)

BT - Proceedings of the GECCO-2000 Graduate Student Workshop

ER -

Some dimensions of problem complexity for XCS

Abstract

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