Learning Classifier Systems: From Foundations to Applications (2000) (Lecture Notes in Computer Science #1813)

By: Pier L. Lanzi and Wolfgang Stolzmann and Stewart W. Wilson

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Synopsis: Learning Classifier Systems (LCS) are a machine learning paradigm introduced by John Holland in 1976. They are rule-based systems in which learning is viewed as a process of ongoing adaptation to a partially unknown environment through genetic algorithms and temporal difference learning. This book provides a unique survey of the current state of the art of LCS and highlights some of the most promising research directions. The first part presents various views of leading people on what learning classifier systems are. The second part is devoted to advanced topics of current interest, including alternative representations, methods for evaluating rule utility, and extensions to existing classifier system models. The final part is dedicated to promising applications in areas like data mining, medical data analysis, economic trading agents, aircraft maneuvering, and autonomous robotics. An appendix comprising 467 entries provides a comprehensive LCS bibliography.

Book Quality:: Publisher Quality
ISBN-13:: 9783540450276
Related ISBNs:: 9783540677291
Publisher:: Springer Berlin Heidelberg
Date of Addition:: 08/22/22
Copyrighted By:: N/A
Adult content:: No
Language:: English
Has Image Descriptions:: No
Categories:: Nonfiction, Computers and Internet, Mathematics and Statistics, Philosophy
Submitted By:: Bookshare Staff
Usage Restrictions:: This is a copyrighted book.
Edited by:: Pier L. Lanzi
Edited by:: Wolfgang Stolzmann
Edited by:: Stewart W. Wilson