Learning Classifier Systems | International Workshops, IWLCS 2003-2005, Revised Selected Papers

Tim Kovacs (u. a.)

ISBN 10: 3540712305 / ISBN 13: 9783540712305

Published by Springer, 2007

About this Item

Learning Classifier Systems | International Workshops, IWLCS 2003-2005, Revised Selected Papers | Tim Kovacs (u. a.) | Taschenbuch | xii | Englisch | 2007 | Springer | EAN 9783540712305 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Seller Inventory # 102066165

Bibliographic Details

• Title: Learning Classifier Systems | International Workshops, IWLCS 2003-2005, Revised Selected Papers
• Publisher: Springer
• Publication Date: 2007
• Language: English
• ISBN 10: 3540712305
• ISBN 13: 9783540712305
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 155H x 20D millimeters 235 millimeters width by 155 millimeters height by 20 millimeters depth
• Weight: 552 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

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.