Lecture Notes in Artificial Intelligence

Learning Classifier Systems

International Workshops, IWLCS 2003-2005, Revised Selected Papers

Editors: Kovacs, T., Llorà, X., Takadama, K., Lanzi, P.L., Stolzmann, W., Wilson, S.W. (Eds.)

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About this book

The work embodied in this volume was presented across three consecutive e- tions of the International Workshop on Learning Classi?er Systems that took place in Chicago (2003), Seattle (2004), and Washington (2005). The Genetic and Evolutionary Computation Conference, the main ACM SIGEvo conference, hosted these three editions. The topics presented in this volume summarize the wide spectrum of interests of the Learning Classi?er Systems (LCS) community. The topics range from theoretical analysis of mechanisms to practical cons- eration for successful application of such techniques to everyday data-mining tasks. When we started editing this volume, we faced the choice of organizing the contents in a purely chronologicalfashion or as a sequence of related topics that help walk the reader across the di?erent areas. In the end we decided to or- nize the contents by area, breaking the time-line a little. This is not a simple endeavor as we can organize the material using multiple criteria. The tax- omy below is our humble e?ort to provide a coherent grouping. Needless to say, some works may fall in more than one category. The four areas are as follows: Knowledge representation. These chapters elaborate on the knowledge r- resentations used in LCS. Knowledge representation is a key issue in any learning system and has implications for what it is possible to learn and what mechanisms shouldbe used. Four chapters analyze di?erent knowledge representations and the LCS methods used to manipulate them.

Table of contents (22 chapters)

  • Analyzing Parameter Sensitivity and Classifier Representations for Real-Valued XCS

    Wada, Atsushi (et al.)

    Pages 1-16

  • Use of Learning Classifier System for Inferring Natural Language Grammar

    Unold, Olgierd (et al.)

    Pages 17-24

  • Backpropagation in Accuracy-Based Neural Learning Classifier Systems

    O’Hara, Toby (et al.)

    Pages 25-39

  • Binary Rule Encoding Schemes: A Study Using the Compact Classifier System

    Llorà, Xavier (et al.)

    Pages 40-58

  • Bloat Control and Generalization Pressure Using the Minimum Description Length Principle for a Pittsburgh Approach Learning Classifier System

    Bacardit, Jaume (et al.)

    Pages 59-79

Buy this book

eBook $79.99
price for USA (gross)
  • ISBN 978-3-540-71231-2
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.00
price for USA
  • ISBN 978-3-540-71230-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
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Bibliographic Information

Bibliographic Information
Book Title
Learning Classifier Systems
Book Subtitle
International Workshops, IWLCS 2003-2005, Revised Selected Papers
Editors
  • Tim Kovacs
  • Xavier Llorà
  • Keiki Takadama
  • Pier Luca Lanzi
  • Wolfgang Stolzmann
  • Stewart W. Wilson
Series Title
Lecture Notes in Artificial Intelligence
Series Volume
4399
Copyright
2007
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-540-71231-2
DOI
10.1007/978-3-540-71231-2
Softcover ISBN
978-3-540-71230-5
Edition Number
1
Number of Pages
XII, 345
Topics