Learning Classifier Systems From Foundations to Applications

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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 details

Edition:
2000
Series:
Lecture Notes in Computer Science (Book 1813)
Author:
Pier L. Lanzi, Wolfgang Stolzmann, Stewart W. Wilson
ISBN:
9783540450276
Related ISBNs:
9783540677291
Publisher:
Springer Berlin Heidelberg
Pages:
N/A
Reading age:
Not specified
Includes images:
No
Date of addition:
2022-08-22
Usage restrictions:
Copyright
Copyright date:
2000
Copyright by:
N/A 
Adult content:
No
Language:
English
Categories:
Computers and Internet, Mathematics and Statistics, Nonfiction, Philosophy