Overview
- Recent theoretical work in Learning Classifier Systems (LCS)
- Presents a coherent framework of LCS
- Includes a relevant historical original work by John Holland
- Includes supplementary material: sn.pub/extras
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 183)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (13 chapters)
-
Front Matter
Keywords
About this book
This volume brings together recent theoretical work in Learning Classifier Systems (LCS), which is a Machine Learning technique combining Genetic Algorithms and Reinforcement Learning. It includes self-contained background chapters on related fields (reinforcement learning and evolutionary computation) tailored for a classifier systems audience and written by acknowledged authorities in their area - as well as a relevant historical original work by John Holland.
Bibliographic Information
Book Title: Foundations of Learning Classifier Systems
Editors: Larry Bull, Tim Kovacs
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/b100387
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2005
Hardcover ISBN: 978-3-540-25073-9Published: 22 July 2005
Softcover ISBN: 978-3-642-06413-5Published: 25 November 2010
eBook ISBN: 978-3-540-32396-9Published: 26 July 2005
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: VI, 336
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Applications of Mathematics, Bioinformatics
Industry Sectors: Aerospace, Automotive, Biotechnology, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Oil, Gas & Geosciences, Telecommunications