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MILCS: a mutual information learning classifier system
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Genetic And Evolutionary Computation Conference archive
Proceedings of the 9th annual conference on Genetic and evolutionary computation table of contents
London, England
POSTER SESSION: Genetics-based machine learning: posters table of contents
Pages: 1877 - 1877  
Year of Publication: 2007
ISBN:978-1-59593-697-4
Authors
Robert E. Smith  University College London, London, United Kingdom
Max Kun Jiang  University College London, London, United Kingdom
Sponsors
SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 1,   Downloads (12 Months): 13,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

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ABSTRACT

This paper introduces a new variety of learning classifier system (LCS), called MILCS, with mutual information fitness.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
1
Butz, M. V.(203) Documentation of XCS+TS C-Code 1.2. IlliGAL report 2003023, University of Illinois at Urbana- Champaign.
 
2
Scott E. Fahlman , Christian Lebiere, The cascade-correlation learning architecture, Advances in neural information processing systems 2, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1990
 
3
Shannon, C.E. (1948). A Mathematical Theory of Communication, Bell System Technical Journal, 27, pp. 379--423 & 623--656, July & October, 1948
 
4
Smith, R. E. and Jiang, M. K. (submitted). A Learning Classifier System with Mutual-Information-Based Fitness

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.6 Learning


General Terms:
Algorithms


Keywords:
LCS, XCS, information theory, learning classifier systems, mutual information, structural learning

Collaborative Colleagues:
Robert E. Smith: colleagues
Max Kun Jiang: colleagues

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